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<title>1. Simple Features for R</title>

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<h1 class="title toc-ignore">1. Simple Features for R</h1>
<h4 class="author">Edzer Pebesma</h4>



<p><strong>For a better version of the sf vignettes see</strong> <a href="https://r-spatial.github.io/sf/articles/" class="uri">https://r-spatial.github.io/sf/articles/</a></p>
<p><a href="https://en.wikipedia.org/wiki/Simple_Features">Simple
features</a> or <a href="https://www.ogc.org/standard/sfa/"><em>simple
feature access</em></a> refers to a formal standard (ISO 19125-1:2004)
that describes how objects in the real world can be represented in
computers, with emphasis on the <em>spatial</em> geometry of these
objects. It also describes how such objects can be stored in and
retrieved from databases, and which geometrical operations should be
defined for them.</p>
<p>The standard is widely implemented in spatial databases (such as
PostGIS), commercial GIS (e.g., <a href="https://www.esri.com/en-us/home">ESRI ArcGIS</a>) and forms the
vector data basis for libraries such as <a href="https://gdal.org/">GDAL</a>. A subset of simple features forms the
<a href="https://geojson.org/">GeoJSON</a> standard.</p>
<p>R has well-supported classes for storing spatial data (<a href="https://CRAN.R-project.org/package=sp">sp</a>) and interfacing to
the above mentioned environments (<a href="https://CRAN.R-project.org/package=rgdal">rgdal</a>, <a href="https://CRAN.R-project.org/package=rgeos">rgeos</a>), but has so
far lacked a complete implementation of simple features, making
conversions at times convoluted, inefficient or incomplete. The package
<a href="https://github.com/r-spatial/sf">sf</a> tries to fill this gap,
and aims at succeeding <a href="https://CRAN.R-project.org/package=sp">sp</a> in the long
term.</p>
<p>This vignette:</p>
<ul>
<li>explains what is meant by features, and by simple features</li>
<li>shows how they are implemented in R</li>
<li>provides examples of how you can work with them</li>
<li>shows how they can be read from and written to external files or
resources</li>
<li>discusses how they can be converted to and from sp objects</li>
<li>shows how they can be used for meaningful spatial analysis</li>
</ul>
<div id="what-is-a-feature" class="section level1">
<h1>What is a feature?</h1>
<p>A feature is thought of as a thing, or an object in the real world,
such as a building or a tree. As is the case with objects, they often
consist of other objects. This is the case with features too: a set of
features can form a single feature. A forest stand can be a feature, a
forest can be a feature, a city can be a feature. A satellite image
pixel can be a feature, a complete image can be a feature too.</p>
<p>Features have a <em>geometry</em> describing <em>where</em> on Earth
the feature is located, and they have attributes, which describe other
properties. The geometry of a tree can be the delineation of its crown,
of its stem, or the point indicating its centre. Other properties may
include its height, color, diameter at breast height at a particular
date, and so on.</p>
<p>The standard says: “<em>A simple feature is defined by the OpenGIS
Abstract specification to have both spatial and non-spatial attributes.
Spatial attributes are geometry valued, and simple features are based on
2D geometry with linear interpolation between vertices.</em>” We will
see soon that the same standard will extend its coverage beyond 2D and
beyond linear interpolation. Here, we take simple features as the data
structures and operations described in the <a href="https://www.ogc.org/standard/sfa/">standard</a>.</p>
<div id="dimensions" class="section level2">
<h2>Dimensions</h2>
<p>All geometries are composed of points. Points are coordinates in a
2-, 3- or 4-dimensional space. All points in a geometry have the same
dimensionality. In addition to X and Y coordinates, there are two
optional additional dimensions:</p>
<ul>
<li>a Z coordinate, denoting altitude</li>
<li>an M coordinate (rarely used), denoting some <em>measure</em> that
is associated with the point, rather than with the feature as a whole
(in which case it would be a feature attribute); examples could be time
of measurement, or measurement error of the coordinates</li>
</ul>
<p>The four possible cases then are:</p>
<ol style="list-style-type: decimal">
<li>two-dimensional points refer to x and y, easting and northing, or
longitude and latitude, we refer to them as XY</li>
<li>three-dimensional points as XYZ</li>
<li>three-dimensional points as XYM</li>
<li>four-dimensional points as XYZM (the third axis is Z, fourth M)</li>
</ol>
</div>
<div id="simple-feature-geometry-types" class="section level2">
<h2>Simple feature geometry types</h2>
<p>The following seven simple feature types are the most common, and are
for instance the only ones used for <a href="https://www.rfc-editor.org/rfc/rfc7946">GeoJSON</a>:</p>
<table>
<colgroup>
<col width="7%" />
<col width="92%" />
</colgroup>
<thead>
<tr class="header">
<th>type</th>
<th>description</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>POINT</code></td>
<td>zero-dimensional geometry containing a single point</td>
</tr>
<tr class="even">
<td><code>LINESTRING</code></td>
<td>sequence of points connected by straight, non-self intersecting line
pieces; one-dimensional geometry</td>
</tr>
<tr class="odd">
<td><code>POLYGON</code></td>
<td>geometry with a positive area (two-dimensional); sequence of points
form a closed, non-self intersecting ring; the first ring denotes the
exterior ring, zero or more subsequent rings denote holes in this
exterior ring</td>
</tr>
<tr class="even">
<td><code>MULTIPOINT</code></td>
<td>set of points; a MULTIPOINT is simple if no two Points in the
MULTIPOINT are equal</td>
</tr>
<tr class="odd">
<td><code>MULTILINESTRING</code></td>
<td>set of linestrings</td>
</tr>
<tr class="even">
<td><code>MULTIPOLYGON</code></td>
<td>set of polygons</td>
</tr>
<tr class="odd">
<td><code>GEOMETRYCOLLECTION</code></td>
<td>set of geometries of any type except GEOMETRYCOLLECTION</td>
</tr>
</tbody>
</table>
<p>Each of the geometry types can also be a (typed) empty set,
containing zero coordinates (for <code>POINT</code> the standard is not
clear how to represent the empty geometry). Empty geometries can be
thought of being the analogue to missing (<code>NA</code>) attributes,
NULL values or empty lists.</p>
<p>The remaining geometries 10 are rarer, but increasingly find
implementations:</p>
<table>
<colgroup>
<col width="7%" />
<col width="92%" />
</colgroup>
<thead>
<tr class="header">
<th>type</th>
<th>description</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>CIRCULARSTRING</code></td>
<td>The CIRCULARSTRING is the basic curve type, similar to a LINESTRING
in the linear world. A single segment requires three points, the start
and end points (first and third) and any other point on the arc. The
exception to this is for a closed circle, where the start and end points
are the same. In this case the second point MUST be the center of the
arc, i.e., the opposite side of the circle. To chain arcs together, the
last point of the previous arc becomes the first point of the next arc,
just like in LINESTRING. This means that a valid circular string must
have an odd number of points greater than 1.</td>
</tr>
<tr class="even">
<td><code>COMPOUNDCURVE</code></td>
<td>A compound curve is a single, continuous curve that has both curved
(circular) segments and linear segments. That means that in addition to
having well-formed components, the end point of every component (except
the last) must be coincident with the start point of the following
component.</td>
</tr>
<tr class="odd">
<td><code>CURVEPOLYGON</code></td>
<td>Example compound curve in a curve polygon:
CURVEPOLYGON(COMPOUNDCURVE(CIRCULARSTRING(0 0,2 0, 2 1, 2 3, 4 3),(4 3,
4 5, 1 4, 0 0)), CIRCULARSTRING(1.7 1, 1.4 0.4, 1.6 0.4, 1.6 0.5, 1.7 1)
)</td>
</tr>
<tr class="even">
<td><code>MULTICURVE</code></td>
<td>A MultiCurve is a 1-dimensional GeometryCollection whose elements
are Curves, it can include linear strings, circular strings or compound
strings.</td>
</tr>
<tr class="odd">
<td><code>MULTISURFACE</code></td>
<td>A MultiSurface is a 2-dimensional GeometryCollection whose elements
are Surfaces, all using coordinates from the same coordinate reference
system.</td>
</tr>
<tr class="even">
<td><code>CURVE</code></td>
<td>A Curve is a 1-dimensional geometric object usually stored as a
sequence of Points, with the subtype of Curve specifying the form of the
interpolation between Points</td>
</tr>
<tr class="odd">
<td><code>SURFACE</code></td>
<td>A Surface is a 2-dimensional geometric object</td>
</tr>
<tr class="even">
<td><code>POLYHEDRALSURFACE</code></td>
<td>A PolyhedralSurface is a contiguous collection of polygons, which
share common boundary segments</td>
</tr>
<tr class="odd">
<td><code>TIN</code></td>
<td>A TIN (triangulated irregular network) is a PolyhedralSurface
consisting only of Triangle patches.</td>
</tr>
<tr class="even">
<td><code>TRIANGLE</code></td>
<td>A Triangle is a polygon with 3 distinct, non-collinear vertices and
no interior boundary</td>
</tr>
</tbody>
</table>
<p>Note that <code>CIRCULASTRING</code>, <code>COMPOUNDCURVE</code> and
<code>CURVEPOLYGON</code> are not described in the SFA standard, but in
the <a href="https://www.iso.org/standard/38651.html">SQL-MM part 3
standard</a>. The descriptions above were copied from the <a href="http://postgis.net/docs/using_postgis_dbmanagement.html">PostGIS
manual</a>.</p>
</div>
<div id="coordinate-reference-system" class="section level2">
<h2>Coordinate reference system</h2>
<p>Coordinates can only be placed on the Earth’s surface when their
coordinate reference system (CRS) is known; this may be a spheroid CRS
such as WGS84, a projected, two-dimensional (Cartesian) CRS such as a
UTM zone or Web Mercator, or a CRS in three-dimensions, or including
time. Similarly, M-coordinates need an attribute reference system,
e.g. a <a href="https://CRAN.R-project.org/package=units">measurement
unit</a>.</p>
</div>
</div>
<div id="how-simple-features-in-r-are-organized" class="section level1">
<h1>How simple features in R are organized</h1>
<p>Package <code>sf</code> represents simple features as native R
objects. Similar to <a href="http://postgis.net/">PostGIS</a>, all
functions and methods in <code>sf</code> that operate on spatial data
are prefixed by <code>st_</code>, which refers to <em>spatial type</em>;
this makes them easily findable by command-line completion. Simple
features are implemented as R native data, using simple data structures
(S3 classes, lists, matrix, vector). Typical use involves reading,
manipulating and writing of sets of features, with attributes and
geometries.</p>
<p>As attributes are typically stored in <code>data.frame</code> objects
(or the very similar <code>tbl_df</code>), we will also store feature
geometries in a <code>data.frame</code> column. Since geometries are not
single-valued, they are put in a list-column, a list of length equal to
the number of records in the <code>data.frame</code>, with each list
element holding the simple feature geometry of that feature. The three
classes used to represent simple features are:</p>
<ul>
<li><code>sf</code>, the table (<code>data.frame</code>) with feature
attributes and feature geometries, which contains</li>
<li><code>sfc</code>, the list-column with the geometries for each
feature (record), which is composed of</li>
<li><code>sfg</code>, the feature geometry of an individual simple
feature.</li>
</ul>
<p>We will now discuss each of these three classes.</p>
<div id="sf-objects-with-simple-features" class="section level2">
<h2>sf: objects with simple features</h2>
<p>As we usually do not work with geometries of single simple features,
but with datasets consisting of sets of features with attributes, the
two are put together in <code>sf</code> (simple feature) objects. The
following command reads the <code>nc</code> dataset from a file that is
contained in the <code>sf</code> package:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(sf)</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="do">## Linking to GEOS 3.11.1, GDAL 3.6.2, PROJ 9.1.1; sf_use_s2() is TRUE</span></span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>nc <span class="ot">&lt;-</span> <span class="fu">st_read</span>(<span class="fu">system.file</span>(<span class="st">&quot;shape/nc.shp&quot;</span>, <span class="at">package=</span><span class="st">&quot;sf&quot;</span>))</span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="do">## Reading layer `nc&#39; from data source </span></span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="do">##   `/tmp/RtmpoyoHrq/Rinstb81f123adcb20/sf/shape/nc.shp&#39; using driver `ESRI Shapefile&#39;</span></span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="do">## Simple feature collection with 100 features and 14 fields</span></span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="do">## Geometry type: MULTIPOLYGON</span></span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="do">## Dimension:     XY</span></span>
<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="do">## Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965</span></span>
<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a><span class="do">## Geodetic CRS:  NAD27</span></span></code></pre></div>
<p>(Note that users will not use <code>system.file</code> but give a
<code>filename</code> directly, and that shapefiles consist of more than
one file, all with identical basename, which reside in the same
directory.) The short report printed gives the file name, the driver
(ESRI Shapefile), mentions that there are 100 features (records,
represented as rows) and 14 fields (attributes, represented as columns).
This object is of class</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">class</span>(nc)</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] &quot;sf&quot;         &quot;data.frame&quot;</span></span></code></pre></div>
<p>meaning it extends (and “is” a) <code>data.frame</code>, but with a
single list-column with geometries, which is held in the column with
name</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">attr</span>(nc, <span class="st">&quot;sf_column&quot;</span>)</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] &quot;geometry&quot;</span></span></code></pre></div>
<p>If we print the first three features, we see their attribute values
and an abridged version of the geometry</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">print</span>(nc[<span class="dv">9</span><span class="sc">:</span><span class="dv">15</span>], <span class="at">n =</span> <span class="dv">3</span>)</span></code></pre></div>
<p>which would give the following output:</p>
<p><img src="data:image/png;base64,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" /></p>
<p>In the output we see:</p>
<ul>
<li>in green a simple feature: a single record, or
<code>data.frame</code> row, consisting of attributes and geometry</li>
<li>in blue a single simple feature geometry (an object of class
<code>sfg</code>)</li>
<li>in red a simple feature list-column (an object of class
<code>sfc</code>, which is a column in the <code>data.frame</code>)</li>
<li>that although geometries are native R objects, they are printed as
<a href="#wkb">well-known text</a></li>
</ul>
<p>Methods for <code>sf</code> objects are</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="fu">methods</span>(<span class="at">class =</span> <span class="st">&quot;sf&quot;</span>)</span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="do">##  [1] $&lt;-                          [                           </span></span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a><span class="do">##  [3] [[&lt;-                         aggregate                   </span></span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a><span class="do">##  [5] as.data.frame                cbind                       </span></span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a><span class="do">##  [7] coerce                       dbDataType                  </span></span>
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a><span class="do">##  [9] dbWriteTable                 filter                      </span></span>
<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a><span class="do">## [11] identify                     initialize                  </span></span>
<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a><span class="do">## [13] merge                        plot                        </span></span>
<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a><span class="do">## [15] print                        rbind                       </span></span>
<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a><span class="do">## [17] show                         slotsFromS3                 </span></span>
<span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a><span class="do">## [19] st_agr                       st_agr&lt;-                    </span></span>
<span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a><span class="do">## [21] st_area                      st_as_s2                    </span></span>
<span id="cb5-13"><a href="#cb5-13" aria-hidden="true" tabindex="-1"></a><span class="do">## [23] st_as_sf                     st_as_sfc                   </span></span>
<span id="cb5-14"><a href="#cb5-14" aria-hidden="true" tabindex="-1"></a><span class="do">## [25] st_bbox                      st_boundary                 </span></span>
<span id="cb5-15"><a href="#cb5-15" aria-hidden="true" tabindex="-1"></a><span class="do">## [27] st_break_antimeridian        st_buffer                   </span></span>
<span id="cb5-16"><a href="#cb5-16" aria-hidden="true" tabindex="-1"></a><span class="do">## [29] st_cast                      st_centroid                 </span></span>
<span id="cb5-17"><a href="#cb5-17" aria-hidden="true" tabindex="-1"></a><span class="do">## [31] st_collection_extract        st_concave_hull             </span></span>
<span id="cb5-18"><a href="#cb5-18" aria-hidden="true" tabindex="-1"></a><span class="do">## [33] st_convex_hull               st_coordinates              </span></span>
<span id="cb5-19"><a href="#cb5-19" aria-hidden="true" tabindex="-1"></a><span class="do">## [35] st_crop                      st_crs                      </span></span>
<span id="cb5-20"><a href="#cb5-20" aria-hidden="true" tabindex="-1"></a><span class="do">## [37] st_crs&lt;-                     st_difference               </span></span>
<span id="cb5-21"><a href="#cb5-21" aria-hidden="true" tabindex="-1"></a><span class="do">## [39] st_drop_geometry             st_filter                   </span></span>
<span id="cb5-22"><a href="#cb5-22" aria-hidden="true" tabindex="-1"></a><span class="do">## [41] st_geometry                  st_geometry&lt;-               </span></span>
<span id="cb5-23"><a href="#cb5-23" aria-hidden="true" tabindex="-1"></a><span class="do">## [43] st_inscribed_circle          st_interpolate_aw           </span></span>
<span id="cb5-24"><a href="#cb5-24" aria-hidden="true" tabindex="-1"></a><span class="do">## [45] st_intersection              st_intersects               </span></span>
<span id="cb5-25"><a href="#cb5-25" aria-hidden="true" tabindex="-1"></a><span class="do">## [47] st_is                        st_is_valid                 </span></span>
<span id="cb5-26"><a href="#cb5-26" aria-hidden="true" tabindex="-1"></a><span class="do">## [49] st_join                      st_line_merge               </span></span>
<span id="cb5-27"><a href="#cb5-27" aria-hidden="true" tabindex="-1"></a><span class="do">## [51] st_m_range                   st_make_valid               </span></span>
<span id="cb5-28"><a href="#cb5-28" aria-hidden="true" tabindex="-1"></a><span class="do">## [53] st_minimum_rotated_rectangle st_nearest_points           </span></span>
<span id="cb5-29"><a href="#cb5-29" aria-hidden="true" tabindex="-1"></a><span class="do">## [55] st_node                      st_normalize                </span></span>
<span id="cb5-30"><a href="#cb5-30" aria-hidden="true" tabindex="-1"></a><span class="do">## [57] st_point_on_surface          st_polygonize               </span></span>
<span id="cb5-31"><a href="#cb5-31" aria-hidden="true" tabindex="-1"></a><span class="do">## [59] st_precision                 st_reverse                  </span></span>
<span id="cb5-32"><a href="#cb5-32" aria-hidden="true" tabindex="-1"></a><span class="do">## [61] st_sample                    st_segmentize               </span></span>
<span id="cb5-33"><a href="#cb5-33" aria-hidden="true" tabindex="-1"></a><span class="do">## [63] st_set_precision             st_shift_longitude          </span></span>
<span id="cb5-34"><a href="#cb5-34" aria-hidden="true" tabindex="-1"></a><span class="do">## [65] st_simplify                  st_snap                     </span></span>
<span id="cb5-35"><a href="#cb5-35" aria-hidden="true" tabindex="-1"></a><span class="do">## [67] st_sym_difference            st_transform                </span></span>
<span id="cb5-36"><a href="#cb5-36" aria-hidden="true" tabindex="-1"></a><span class="do">## [69] st_triangulate               st_triangulate_constrained  </span></span>
<span id="cb5-37"><a href="#cb5-37" aria-hidden="true" tabindex="-1"></a><span class="do">## [71] st_union                     st_voronoi                  </span></span>
<span id="cb5-38"><a href="#cb5-38" aria-hidden="true" tabindex="-1"></a><span class="do">## [73] st_wrap_dateline             st_write                    </span></span>
<span id="cb5-39"><a href="#cb5-39" aria-hidden="true" tabindex="-1"></a><span class="do">## [75] st_z_range                   st_zm                       </span></span>
<span id="cb5-40"><a href="#cb5-40" aria-hidden="true" tabindex="-1"></a><span class="do">## [77] transform                   </span></span>
<span id="cb5-41"><a href="#cb5-41" aria-hidden="true" tabindex="-1"></a><span class="do">## see &#39;?methods&#39; for accessing help and source code</span></span></code></pre></div>
<p>It is also possible to create <code>data.frame</code> objects with
geometry list-columns that are not of class <code>sf</code>, e.g. by</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>nc.no_sf <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(nc)</span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="fu">class</span>(nc.no_sf)</span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] &quot;data.frame&quot;</span></span></code></pre></div>
<p>However, such objects:</p>
<ul>
<li>no longer register which column is the geometry list-column</li>
<li>no longer have a plot method, and</li>
<li>lack all of the other dedicated methods listed above for class
<code>sf</code></li>
</ul>
</div>
<div id="sfc-simple-feature-geometry-list-column" class="section level2">
<h2>sfc: simple feature geometry list-column</h2>
<p>The column in the <code>sf</code> data.frame that contains the
geometries is a list, of class <code>sfc</code>. We can retrieve the
geometry list-column in this case by <code>nc$geom</code> or
<code>nc[[15]]</code>, but the more general way uses
<code>st_geometry</code>:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>(nc_geom <span class="ot">&lt;-</span> <span class="fu">st_geometry</span>(nc))</span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="do">## Geometry set for 100 features </span></span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="do">## Geometry type: MULTIPOLYGON</span></span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a><span class="do">## Dimension:     XY</span></span>
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a><span class="do">## Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965</span></span>
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a><span class="do">## Geodetic CRS:  NAD27</span></span>
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a><span class="do">## First 5 geometries:</span></span>
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a><span class="do">## MULTIPOLYGON (((-81.47276 36.23436, -81.54084 3...</span></span>
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a><span class="do">## MULTIPOLYGON (((-81.23989 36.36536, -81.24069 3...</span></span>
<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a><span class="do">## MULTIPOLYGON (((-80.45634 36.24256, -80.47639 3...</span></span>
<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a><span class="do">## MULTIPOLYGON (((-76.00897 36.3196, -76.01735 36...</span></span>
<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a><span class="do">## MULTIPOLYGON (((-77.21767 36.24098, -77.23461 3...</span></span></code></pre></div>
<p>Geometries are printed in abbreviated form, but we can view a
complete geometry by selecting it, e.g. the first one by</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>nc_geom[[<span class="dv">1</span>]]</span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="do">## MULTIPOLYGON (((-81.47276 36.23436, -81.54084 36.27251, -81.56198 36.27359, -81.63306 36.34069, -81.74107 36.39178, -81.69828 36.47178, -81.7028 36.51934, -81.67 36.58965, -81.3453 36.57286, -81.34754 36.53791, -81.32478 36.51368, -81.31332 36.4807, -81.26624 36.43721, -81.26284 36.40504, -81.24069 36.37942, -81.23989 36.36536, -81.26424 36.35241, -81.32899 36.3635, -81.36137 36.35316, -81.36569 36.33905, -81.35413 36.29972, -81.36745 36.2787, -81.40639 36.28505, -81.41233 36.26729, -81.43104 36.26072, -81.45289 36.23959, -81.47276 36.23436)))</span></span></code></pre></div>
<p>The way this is printed is called <em>well-known text</em>, and is
part of the standards. The word <code>MULTIPOLYGON</code> is followed by
three parentheses, because it can consist of multiple polygons, in the
form of <code>MULTIPOLYGON(POL1,POL2)</code>, where <code>POL1</code>
might consist of an exterior ring and zero or more interior rings, as of
<code>(EXT1,HOLE1,HOLE2)</code>. Sets of coordinates are held together
with parentheses, so we get
<code>((crds_ext)(crds_hole1)(crds_hole2))</code> where
<code>crds_</code> is a comma-separated set of coordinates of a ring.
This leads to the case above, where
<code>MULTIPOLYGON(((crds_ext)))</code> refers to the exterior ring (1),
without holes (2), of the first polygon (3) - hence three
parentheses.</p>
<p>We can see there is a single polygon with no rings:</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">par</span>(<span class="at">mar =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>))</span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(nc[<span class="dv">1</span>], <span class="at">reset =</span> <span class="cn">FALSE</span>) <span class="co"># reset = FALSE: we want to add to a plot with a legend</span></span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(nc[<span class="dv">1</span>,<span class="dv">1</span>], <span class="at">col =</span> <span class="st">&#39;grey&#39;</span>, <span class="at">add =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<p>but some of the polygons in this dataset have multiple exterior
rings; they can be identified by</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="fu">par</span>(<span class="at">mar =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>))</span>
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>(w <span class="ot">&lt;-</span> <span class="fu">which</span>(<span class="fu">sapply</span>(nc_geom, length) <span class="sc">&gt;</span> <span class="dv">1</span>))</span>
<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a><span class="do">## [1]  4 56 57 87 91 95</span></span>
<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(nc[w,<span class="dv">1</span>], <span class="at">col =</span> <span class="dv">2</span><span class="sc">:</span><span class="dv">7</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>Following the <code>MULTIPOLYGON</code> datastructure, in R we have a
list of lists of lists of matrices. For instance, we get the first 3
coordinate pairs of the second exterior ring (first ring is always
exterior) for the geometry of feature 4 by</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>nc_geom[[<span class="dv">4</span>]][[<span class="dv">2</span>]][[<span class="dv">1</span>]][<span class="dv">1</span><span class="sc">:</span><span class="dv">3</span>,]</span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a><span class="do">##           [,1]     [,2]</span></span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a><span class="do">## [1,] -76.02717 36.55672</span></span>
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a><span class="do">## [2,] -75.99866 36.55665</span></span>
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a><span class="do">## [3,] -75.91192 36.54253</span></span></code></pre></div>
<p>Geometry columns have their own class,</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="fu">class</span>(nc_geom)</span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] &quot;sfc_MULTIPOLYGON&quot; &quot;sfc&quot;</span></span></code></pre></div>
<p>Methods for geometry list-columns include</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="fu">methods</span>(<span class="at">class =</span> <span class="st">&#39;sfc&#39;</span>)</span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a><span class="do">##  [1] Ops                          [                           </span></span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a><span class="do">##  [3] [&lt;-                          as.data.frame               </span></span>
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a><span class="do">##  [5] c                            coerce                      </span></span>
<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a><span class="do">##  [7] format                       identify                    </span></span>
<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a><span class="do">##  [9] initialize                   print                       </span></span>
<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a><span class="do">## [11] rep                          show                        </span></span>
<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a><span class="do">## [13] slotsFromS3                  st_area                     </span></span>
<span id="cb13-9"><a href="#cb13-9" aria-hidden="true" tabindex="-1"></a><span class="do">## [15] st_as_binary                 st_as_grob                  </span></span>
<span id="cb13-10"><a href="#cb13-10" aria-hidden="true" tabindex="-1"></a><span class="do">## [17] st_as_s2                     st_as_sf                    </span></span>
<span id="cb13-11"><a href="#cb13-11" aria-hidden="true" tabindex="-1"></a><span class="do">## [19] st_as_text                   st_bbox                     </span></span>
<span id="cb13-12"><a href="#cb13-12" aria-hidden="true" tabindex="-1"></a><span class="do">## [21] st_boundary                  st_break_antimeridian       </span></span>
<span id="cb13-13"><a href="#cb13-13" aria-hidden="true" tabindex="-1"></a><span class="do">## [23] st_buffer                    st_cast                     </span></span>
<span id="cb13-14"><a href="#cb13-14" aria-hidden="true" tabindex="-1"></a><span class="do">## [25] st_centroid                  st_collection_extract       </span></span>
<span id="cb13-15"><a href="#cb13-15" aria-hidden="true" tabindex="-1"></a><span class="do">## [27] st_concave_hull              st_convex_hull              </span></span>
<span id="cb13-16"><a href="#cb13-16" aria-hidden="true" tabindex="-1"></a><span class="do">## [29] st_coordinates               st_crop                     </span></span>
<span id="cb13-17"><a href="#cb13-17" aria-hidden="true" tabindex="-1"></a><span class="do">## [31] st_crs                       st_crs&lt;-                    </span></span>
<span id="cb13-18"><a href="#cb13-18" aria-hidden="true" tabindex="-1"></a><span class="do">## [33] st_difference                st_geometry                 </span></span>
<span id="cb13-19"><a href="#cb13-19" aria-hidden="true" tabindex="-1"></a><span class="do">## [35] st_inscribed_circle          st_intersection             </span></span>
<span id="cb13-20"><a href="#cb13-20" aria-hidden="true" tabindex="-1"></a><span class="do">## [37] st_intersects                st_is                       </span></span>
<span id="cb13-21"><a href="#cb13-21" aria-hidden="true" tabindex="-1"></a><span class="do">## [39] st_is_valid                  st_line_merge               </span></span>
<span id="cb13-22"><a href="#cb13-22" aria-hidden="true" tabindex="-1"></a><span class="do">## [41] st_m_range                   st_make_valid               </span></span>
<span id="cb13-23"><a href="#cb13-23" aria-hidden="true" tabindex="-1"></a><span class="do">## [43] st_minimum_rotated_rectangle st_nearest_points           </span></span>
<span id="cb13-24"><a href="#cb13-24" aria-hidden="true" tabindex="-1"></a><span class="do">## [45] st_node                      st_normalize                </span></span>
<span id="cb13-25"><a href="#cb13-25" aria-hidden="true" tabindex="-1"></a><span class="do">## [47] st_point_on_surface          st_polygonize               </span></span>
<span id="cb13-26"><a href="#cb13-26" aria-hidden="true" tabindex="-1"></a><span class="do">## [49] st_precision                 st_reverse                  </span></span>
<span id="cb13-27"><a href="#cb13-27" aria-hidden="true" tabindex="-1"></a><span class="do">## [51] st_sample                    st_segmentize               </span></span>
<span id="cb13-28"><a href="#cb13-28" aria-hidden="true" tabindex="-1"></a><span class="do">## [53] st_set_precision             st_shift_longitude          </span></span>
<span id="cb13-29"><a href="#cb13-29" aria-hidden="true" tabindex="-1"></a><span class="do">## [55] st_simplify                  st_snap                     </span></span>
<span id="cb13-30"><a href="#cb13-30" aria-hidden="true" tabindex="-1"></a><span class="do">## [57] st_sym_difference            st_transform                </span></span>
<span id="cb13-31"><a href="#cb13-31" aria-hidden="true" tabindex="-1"></a><span class="do">## [59] st_triangulate               st_triangulate_constrained  </span></span>
<span id="cb13-32"><a href="#cb13-32" aria-hidden="true" tabindex="-1"></a><span class="do">## [61] st_union                     st_voronoi                  </span></span>
<span id="cb13-33"><a href="#cb13-33" aria-hidden="true" tabindex="-1"></a><span class="do">## [63] st_wrap_dateline             st_write                    </span></span>
<span id="cb13-34"><a href="#cb13-34" aria-hidden="true" tabindex="-1"></a><span class="do">## [65] st_z_range                   st_zm                       </span></span>
<span id="cb13-35"><a href="#cb13-35" aria-hidden="true" tabindex="-1"></a><span class="do">## [67] str                          summary                     </span></span>
<span id="cb13-36"><a href="#cb13-36" aria-hidden="true" tabindex="-1"></a><span class="do">## [69] vec_cast.sfc                 vec_ptype2.sfc              </span></span>
<span id="cb13-37"><a href="#cb13-37" aria-hidden="true" tabindex="-1"></a><span class="do">## see &#39;?methods&#39; for accessing help and source code</span></span></code></pre></div>
<p>Coordinate reference systems (<code>st_crs</code> and
<code>st_transform</code>) are discussed in the section on <a href="#crs">coordinate reference systems</a>. <code>st_as_wkb</code> and
<code>st_as_text</code> convert geometry list-columns into
well-known-binary or well-known-text, explained <a href="#wkb">below</a>. <code>st_bbox</code> retrieves the coordinate
bounding box.</p>
<p>Attributes include</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="fu">attributes</span>(nc_geom)</span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a><span class="do">## $n_empty</span></span>
<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] 0</span></span>
<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span>
<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a><span class="do">## $crs</span></span>
<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a><span class="do">## Coordinate Reference System:</span></span>
<span id="cb14-7"><a href="#cb14-7" aria-hidden="true" tabindex="-1"></a><span class="do">##   User input: NAD27 </span></span>
<span id="cb14-8"><a href="#cb14-8" aria-hidden="true" tabindex="-1"></a><span class="do">##   wkt:</span></span>
<span id="cb14-9"><a href="#cb14-9" aria-hidden="true" tabindex="-1"></a><span class="do">## GEOGCRS[&quot;NAD27&quot;,</span></span>
<span id="cb14-10"><a href="#cb14-10" aria-hidden="true" tabindex="-1"></a><span class="do">##     DATUM[&quot;North American Datum 1927&quot;,</span></span>
<span id="cb14-11"><a href="#cb14-11" aria-hidden="true" tabindex="-1"></a><span class="do">##         ELLIPSOID[&quot;Clarke 1866&quot;,6378206.4,294.978698213898,</span></span>
<span id="cb14-12"><a href="#cb14-12" aria-hidden="true" tabindex="-1"></a><span class="do">##             LENGTHUNIT[&quot;metre&quot;,1]]],</span></span>
<span id="cb14-13"><a href="#cb14-13" aria-hidden="true" tabindex="-1"></a><span class="do">##     PRIMEM[&quot;Greenwich&quot;,0,</span></span>
<span id="cb14-14"><a href="#cb14-14" aria-hidden="true" tabindex="-1"></a><span class="do">##         ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],</span></span>
<span id="cb14-15"><a href="#cb14-15" aria-hidden="true" tabindex="-1"></a><span class="do">##     CS[ellipsoidal,2],</span></span>
<span id="cb14-16"><a href="#cb14-16" aria-hidden="true" tabindex="-1"></a><span class="do">##         AXIS[&quot;latitude&quot;,north,</span></span>
<span id="cb14-17"><a href="#cb14-17" aria-hidden="true" tabindex="-1"></a><span class="do">##             ORDER[1],</span></span>
<span id="cb14-18"><a href="#cb14-18" aria-hidden="true" tabindex="-1"></a><span class="do">##             ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],</span></span>
<span id="cb14-19"><a href="#cb14-19" aria-hidden="true" tabindex="-1"></a><span class="do">##         AXIS[&quot;longitude&quot;,east,</span></span>
<span id="cb14-20"><a href="#cb14-20" aria-hidden="true" tabindex="-1"></a><span class="do">##             ORDER[2],</span></span>
<span id="cb14-21"><a href="#cb14-21" aria-hidden="true" tabindex="-1"></a><span class="do">##             ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],</span></span>
<span id="cb14-22"><a href="#cb14-22" aria-hidden="true" tabindex="-1"></a><span class="do">##     ID[&quot;EPSG&quot;,4267]]</span></span>
<span id="cb14-23"><a href="#cb14-23" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span>
<span id="cb14-24"><a href="#cb14-24" aria-hidden="true" tabindex="-1"></a><span class="do">## $class</span></span>
<span id="cb14-25"><a href="#cb14-25" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] &quot;sfc_MULTIPOLYGON&quot; &quot;sfc&quot;             </span></span>
<span id="cb14-26"><a href="#cb14-26" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span>
<span id="cb14-27"><a href="#cb14-27" aria-hidden="true" tabindex="-1"></a><span class="do">## $precision</span></span>
<span id="cb14-28"><a href="#cb14-28" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] 0</span></span>
<span id="cb14-29"><a href="#cb14-29" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span>
<span id="cb14-30"><a href="#cb14-30" aria-hidden="true" tabindex="-1"></a><span class="do">## $bbox</span></span>
<span id="cb14-31"><a href="#cb14-31" aria-hidden="true" tabindex="-1"></a><span class="do">##      xmin      ymin      xmax      ymax </span></span>
<span id="cb14-32"><a href="#cb14-32" aria-hidden="true" tabindex="-1"></a><span class="do">## -84.32385  33.88199 -75.45698  36.58965</span></span></code></pre></div>
</div>
<div id="mixed-geometry-types" class="section level2">
<h2>Mixed geometry types</h2>
<p>The class of <code>nc_geom</code> is
<code>c(&quot;sfc_MULTIPOLYGON&quot;, &quot;sfc&quot;)</code>: <code>sfc</code> is shared
with all geometry types, and <code>sfc_TYPE</code> with
<code>TYPE</code> indicating the type of the particular geometry at
hand.</p>
<p>There are two “special” types: <code>GEOMETRYCOLLECTION</code>, and
<code>GEOMETRY</code>. <code>GEOMETRYCOLLECTION</code> indicates that
each of the geometries may contain a mix of geometry types, as in</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>(mix <span class="ot">&lt;-</span> <span class="fu">st_sfc</span>(<span class="fu">st_geometrycollection</span>(<span class="fu">list</span>(<span class="fu">st_point</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">2</span>))),</span>
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>    <span class="fu">st_geometrycollection</span>(<span class="fu">list</span>(<span class="fu">st_linestring</span>(<span class="fu">matrix</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">4</span>,<span class="dv">2</span>))))))</span>
<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a><span class="do">## Geometry set for 2 features </span></span>
<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a><span class="do">## Geometry type: GEOMETRYCOLLECTION</span></span>
<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a><span class="do">## Dimension:     XY</span></span>
<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a><span class="do">## Bounding box:  xmin: 1 ymin: 2 xmax: 2 ymax: 4</span></span>
<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a><span class="do">## CRS:           NA</span></span>
<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a><span class="do">## GEOMETRYCOLLECTION (POINT (1 2))</span></span>
<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a><span class="do">## GEOMETRYCOLLECTION (LINESTRING (1 3, 2 4))</span></span>
<span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a><span class="fu">class</span>(mix)</span>
<span id="cb15-11"><a href="#cb15-11" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] &quot;sfc_GEOMETRYCOLLECTION&quot; &quot;sfc&quot;</span></span></code></pre></div>
<p>Still, the geometries are here of a single type.</p>
<p>The second <code>GEOMETRY</code>, indicates that the geometries in
the geometry list-column are of varying type:</p>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>(mix <span class="ot">&lt;-</span> <span class="fu">st_sfc</span>(<span class="fu">st_point</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">2</span>), <span class="fu">st_linestring</span>(<span class="fu">matrix</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">4</span>,<span class="dv">2</span>))))</span>
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="do">## Geometry set for 2 features </span></span>
<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a><span class="do">## Geometry type: GEOMETRY</span></span>
<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a><span class="do">## Dimension:     XY</span></span>
<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a><span class="do">## Bounding box:  xmin: 1 ymin: 2 xmax: 2 ymax: 4</span></span>
<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a><span class="do">## CRS:           NA</span></span>
<span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a><span class="do">## POINT (1 2)</span></span>
<span id="cb16-8"><a href="#cb16-8" aria-hidden="true" tabindex="-1"></a><span class="do">## LINESTRING (1 3, 2 4)</span></span>
<span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a><span class="fu">class</span>(mix)</span>
<span id="cb16-10"><a href="#cb16-10" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] &quot;sfc_GEOMETRY&quot; &quot;sfc&quot;</span></span></code></pre></div>
<p>These two are fundamentally different: <code>GEOMETRY</code> is a
superclass without instances, <code>GEOMETRYCOLLECTION</code> is a
geometry instance. <code>GEOMETRY</code> list-columns occur when we read
in a data source with a mix of geometry types.
<code>GEOMETRYCOLLECTION</code> <em>is</em> a single feature’s geometry:
the intersection of two feature polygons may consist of points, lines
and polygons, see the example <a href="#geometrycollection">below</a>.</p>
</div>
<div id="sfg-simple-feature-geometry" class="section level2">
<h2>sfg: simple feature geometry</h2>
<p>Simple feature geometry (<code>sfg</code>) objects carry the geometry
for a single feature, e.g. a point, linestring or polygon.</p>
<p>Simple feature geometries are implemented as R native data, using the
following rules</p>
<ol style="list-style-type: decimal">
<li>a single POINT is a numeric vector</li>
<li>a set of points, e.g. in a LINESTRING or ring of a POLYGON is a
<code>matrix</code>, each row containing a point</li>
<li>any other set is a <code>list</code></li>
</ol>
<p>Creator functions are rarely used in practice, since we typically
bulk read and write spatial data. They are useful for illustration:</p>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>(x <span class="ot">&lt;-</span> <span class="fu">st_point</span>(<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>)))</span>
<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a><span class="do">## POINT (1 2)</span></span>
<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a><span class="fu">str</span>(x)</span>
<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a><span class="do">##  &#39;XY&#39; num [1:2] 1 2</span></span>
<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a>(x <span class="ot">&lt;-</span> <span class="fu">st_point</span>(<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>,<span class="dv">3</span>)))</span>
<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a><span class="do">## POINT Z (1 2 3)</span></span>
<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a><span class="fu">str</span>(x)</span>
<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a><span class="do">##  &#39;XYZ&#39; num [1:3] 1 2 3</span></span>
<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a>(x <span class="ot">&lt;-</span> <span class="fu">st_point</span>(<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>,<span class="dv">3</span>), <span class="st">&quot;XYM&quot;</span>))</span>
<span id="cb17-10"><a href="#cb17-10" aria-hidden="true" tabindex="-1"></a><span class="do">## POINT M (1 2 3)</span></span>
<span id="cb17-11"><a href="#cb17-11" aria-hidden="true" tabindex="-1"></a><span class="fu">str</span>(x)</span>
<span id="cb17-12"><a href="#cb17-12" aria-hidden="true" tabindex="-1"></a><span class="do">##  &#39;XYM&#39; num [1:3] 1 2 3</span></span>
<span id="cb17-13"><a href="#cb17-13" aria-hidden="true" tabindex="-1"></a>(x <span class="ot">&lt;-</span> <span class="fu">st_point</span>(<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>,<span class="dv">3</span>,<span class="dv">4</span>)))</span>
<span id="cb17-14"><a href="#cb17-14" aria-hidden="true" tabindex="-1"></a><span class="do">## POINT ZM (1 2 3 4)</span></span>
<span id="cb17-15"><a href="#cb17-15" aria-hidden="true" tabindex="-1"></a><span class="fu">str</span>(x)</span>
<span id="cb17-16"><a href="#cb17-16" aria-hidden="true" tabindex="-1"></a><span class="do">##  &#39;XYZM&#39; num [1:4] 1 2 3 4</span></span>
<span id="cb17-17"><a href="#cb17-17" aria-hidden="true" tabindex="-1"></a><span class="fu">st_zm</span>(x, <span class="at">drop =</span> <span class="cn">TRUE</span>, <span class="at">what =</span> <span class="st">&quot;ZM&quot;</span>)</span>
<span id="cb17-18"><a href="#cb17-18" aria-hidden="true" tabindex="-1"></a><span class="do">## POINT (1 2)</span></span></code></pre></div>
<p>This means that we can represent 2-, 3- or 4-dimensional coordinates.
All geometry objects inherit from <code>sfg</code> (simple feature
geometry), but also have a type (e.g. <code>POINT</code>), and a
dimension (e.g. <code>XYM</code>) class name. A figure illustrates six
of the seven most common types.</p>
<p>With the exception of the <code>POINT</code> which has a single point
as geometry, the remaining six common single simple feature geometry
types that correspond to single features (single records, or rows in a
<code>data.frame</code>) are created like this</p>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>p <span class="ot">&lt;-</span> <span class="fu">rbind</span>(<span class="fu">c</span>(<span class="fl">3.2</span>,<span class="dv">4</span>), <span class="fu">c</span>(<span class="dv">3</span>,<span class="fl">4.6</span>), <span class="fu">c</span>(<span class="fl">3.8</span>,<span class="fl">4.4</span>), <span class="fu">c</span>(<span class="fl">3.5</span>,<span class="fl">3.8</span>), <span class="fu">c</span>(<span class="fl">3.4</span>,<span class="fl">3.6</span>), <span class="fu">c</span>(<span class="fl">3.9</span>,<span class="fl">4.5</span>))</span>
<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>(mp <span class="ot">&lt;-</span> <span class="fu">st_multipoint</span>(p))</span>
<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a><span class="do">## MULTIPOINT ((3.2 4), (3 4.6), (3.8 4.4), (3.5 3.8), (3.4 3.6), (3.9 4.5))</span></span>
<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a>s1 <span class="ot">&lt;-</span> <span class="fu">rbind</span>(<span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">3</span>),<span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">4</span>),<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">5</span>),<span class="fu">c</span>(<span class="dv">2</span>,<span class="dv">5</span>))</span>
<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a>(ls <span class="ot">&lt;-</span> <span class="fu">st_linestring</span>(s1))</span>
<span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a><span class="do">## LINESTRING (0 3, 0 4, 1 5, 2 5)</span></span>
<span id="cb18-7"><a href="#cb18-7" aria-hidden="true" tabindex="-1"></a>s2 <span class="ot">&lt;-</span> <span class="fu">rbind</span>(<span class="fu">c</span>(<span class="fl">0.2</span>,<span class="dv">3</span>), <span class="fu">c</span>(<span class="fl">0.2</span>,<span class="dv">4</span>), <span class="fu">c</span>(<span class="dv">1</span>,<span class="fl">4.8</span>), <span class="fu">c</span>(<span class="dv">2</span>,<span class="fl">4.8</span>))</span>
<span id="cb18-8"><a href="#cb18-8" aria-hidden="true" tabindex="-1"></a>s3 <span class="ot">&lt;-</span> <span class="fu">rbind</span>(<span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">4.4</span>), <span class="fu">c</span>(<span class="fl">0.6</span>,<span class="dv">5</span>))</span>
<span id="cb18-9"><a href="#cb18-9" aria-hidden="true" tabindex="-1"></a>(mls <span class="ot">&lt;-</span> <span class="fu">st_multilinestring</span>(<span class="fu">list</span>(s1,s2,s3)))</span>
<span id="cb18-10"><a href="#cb18-10" aria-hidden="true" tabindex="-1"></a><span class="do">## MULTILINESTRING ((0 3, 0 4, 1 5, 2 5), (0.2 3, 0.2 4, 1 4.8, 2 4.8), (0 4.4, 0.6 5))</span></span>
<span id="cb18-11"><a href="#cb18-11" aria-hidden="true" tabindex="-1"></a>p1 <span class="ot">&lt;-</span> <span class="fu">rbind</span>(<span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">0</span>), <span class="fu">c</span>(<span class="dv">3</span>,<span class="dv">2</span>), <span class="fu">c</span>(<span class="dv">2</span>,<span class="dv">4</span>), <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">4</span>), <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>))</span>
<span id="cb18-12"><a href="#cb18-12" aria-hidden="true" tabindex="-1"></a>p2 <span class="ot">&lt;-</span> <span class="fu">rbind</span>(<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">1</span>), <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>), <span class="fu">c</span>(<span class="dv">2</span>,<span class="dv">2</span>), <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">1</span>))</span>
<span id="cb18-13"><a href="#cb18-13" aria-hidden="true" tabindex="-1"></a>pol <span class="ot">&lt;-</span><span class="fu">st_polygon</span>(<span class="fu">list</span>(p1,p2))</span>
<span id="cb18-14"><a href="#cb18-14" aria-hidden="true" tabindex="-1"></a>p3 <span class="ot">&lt;-</span> <span class="fu">rbind</span>(<span class="fu">c</span>(<span class="dv">3</span>,<span class="dv">0</span>), <span class="fu">c</span>(<span class="dv">4</span>,<span class="dv">0</span>), <span class="fu">c</span>(<span class="dv">4</span>,<span class="dv">1</span>), <span class="fu">c</span>(<span class="dv">3</span>,<span class="dv">1</span>), <span class="fu">c</span>(<span class="dv">3</span>,<span class="dv">0</span>))</span>
<span id="cb18-15"><a href="#cb18-15" aria-hidden="true" tabindex="-1"></a>p4 <span class="ot">&lt;-</span> <span class="fu">rbind</span>(<span class="fu">c</span>(<span class="fl">3.3</span>,<span class="fl">0.3</span>), <span class="fu">c</span>(<span class="fl">3.8</span>,<span class="fl">0.3</span>), <span class="fu">c</span>(<span class="fl">3.8</span>,<span class="fl">0.8</span>), <span class="fu">c</span>(<span class="fl">3.3</span>,<span class="fl">0.8</span>), <span class="fu">c</span>(<span class="fl">3.3</span>,<span class="fl">0.3</span>))[<span class="dv">5</span><span class="sc">:</span><span class="dv">1</span>,]</span>
<span id="cb18-16"><a href="#cb18-16" aria-hidden="true" tabindex="-1"></a>p5 <span class="ot">&lt;-</span> <span class="fu">rbind</span>(<span class="fu">c</span>(<span class="dv">3</span>,<span class="dv">3</span>), <span class="fu">c</span>(<span class="dv">4</span>,<span class="dv">2</span>), <span class="fu">c</span>(<span class="dv">4</span>,<span class="dv">3</span>), <span class="fu">c</span>(<span class="dv">3</span>,<span class="dv">3</span>))</span>
<span id="cb18-17"><a href="#cb18-17" aria-hidden="true" tabindex="-1"></a>(mpol <span class="ot">&lt;-</span> <span class="fu">st_multipolygon</span>(<span class="fu">list</span>(<span class="fu">list</span>(p1,p2), <span class="fu">list</span>(p3,p4), <span class="fu">list</span>(p5))))</span>
<span id="cb18-18"><a href="#cb18-18" aria-hidden="true" tabindex="-1"></a><span class="do">## MULTIPOLYGON (((0 0, 1 0, 3 2, 2 4, 1 4, 0 0), (1 1, 1 2, 2 2, 1 1)), ((3 0, 4 0, 4 1, 3 1, 3 0), (3.3 0.3, 3.3 0.8, 3.8 0.8, 3.8 0.3, 3.3 0.3)), ((3 3, 4 2, 4 3, 3 3)))</span></span>
<span id="cb18-19"><a href="#cb18-19" aria-hidden="true" tabindex="-1"></a>(gc <span class="ot">&lt;-</span> <span class="fu">st_geometrycollection</span>(<span class="fu">list</span>(mp, mpol, ls)))</span>
<span id="cb18-20"><a href="#cb18-20" aria-hidden="true" tabindex="-1"></a><span class="do">## GEOMETRYCOLLECTION (MULTIPOINT ((3.2 4), (3 4.6), (3.8 4.4), (3.5 3.8), (3.4 3.6), (3.9 4.5)), MULTIPOLYGON (((0 0, 1 0, 3 2, 2 4, 1 4, 0 0), (1 1, 1 2, 2 2, 1 1)), ((3 0, 4 0, 4 1, 3 1, 3 0), (3.3 0.3, 3.3 0.8, 3.8 0.8, 3.8 0.3, 3.3 0.3)), ((3 3, 4 2, 4 3, 3 3))), LINESTRING (0 3, 0 4, 1 5, 2 5))</span></span></code></pre></div>
<p>The objects created are shown here:</p>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>Geometries can also be empty, as in</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>(x <span class="ot">&lt;-</span> <span class="fu">st_geometrycollection</span>())</span>
<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a><span class="do">## GEOMETRYCOLLECTION EMPTY</span></span>
<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a><span class="fu">length</span>(x)</span>
<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] 0</span></span></code></pre></div>
</div>
<div id="wkb" class="section level2">
<h2>Well-known text, well-known binary, precision</h2>
<div id="wkt-and-wkb" class="section level3">
<h3>WKT and WKB</h3>
<p>Well-known text (WKT) and well-known binary (WKB) are two encodings
for simple feature geometries. Well-known text, e.g. seen in</p>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>x <span class="ot">&lt;-</span> <span class="fu">st_linestring</span>(<span class="fu">matrix</span>(<span class="dv">10</span><span class="sc">:</span><span class="dv">1</span>,<span class="dv">5</span>))</span>
<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a><span class="fu">st_as_text</span>(x)</span>
<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] &quot;LINESTRING (10 5, 9 4, 8 3, 7 2, 6 1)&quot;</span></span></code></pre></div>
<p>(but without the leading <code>## [1]</code> and quotes), is
human-readable. Coordinates are usually floating point numbers, and
moving large amounts of information as text is slow and imprecise. For
that reason, we use well-known binary (WKB) encoding</p>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="fu">st_as_binary</span>(x)</span>
<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a><span class="do">##  [1] 01 02 00 00 00 05 00 00 00 00 00 00 00 00 00 24 40 00 00 00 00 00 00 14 40</span></span>
<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a><span class="do">## [26] 00 00 00 00 00 00 22 40 00 00 00 00 00 00 10 40 00 00 00 00 00 00 20 40 00</span></span>
<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a><span class="do">## [51] 00 00 00 00 00 08 40 00 00 00 00 00 00 1c 40 00 00 00 00 00 00 00 40 00 00</span></span>
<span id="cb21-5"><a href="#cb21-5" aria-hidden="true" tabindex="-1"></a><span class="do">## [76] 00 00 00 00 18 40 00 00 00 00 00 00 f0 3f</span></span></code></pre></div>
<p>WKT and WKB can both be transformed back into R native objects by</p>
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="fu">st_as_sfc</span>(<span class="st">&quot;LINESTRING(10 5, 9 4, 8 3, 7 2, 6 1)&quot;</span>)[[<span class="dv">1</span>]]</span>
<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a><span class="do">## LINESTRING (10 5, 9 4, 8 3, 7 2, 6 1)</span></span>
<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a><span class="fu">st_as_sfc</span>(<span class="fu">structure</span>(<span class="fu">list</span>(<span class="fu">st_as_binary</span>(x)), <span class="at">class =</span> <span class="st">&quot;WKB&quot;</span>))[[<span class="dv">1</span>]]</span>
<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a><span class="do">## LINESTRING (10 5, 9 4, 8 3, 7 2, 6 1)</span></span></code></pre></div>
<p>GDAL, GEOS, spatial databases and GIS read and write WKB which is
fast and precise. Conversion between R native objects and WKB is done by
package <code>sf</code> in compiled (C++/Rcpp) code, making this a
reusable and fast route for I/O of simple feature geometries in R.</p>
</div>
<div id="precision" class="section level3">
<h3>Precision</h3>
<p>One of the attributes of a geometry list-column (<code>sfc</code>) is
the <code>precision</code>: a double number that, when non-zero, causes
some rounding during conversion to WKB, which might help certain
geometrical operations succeed that would otherwise fail due to floating
point representation. The model is that of GEOS, which copies from the
Java Topology Suite (<a href="https://locationtech.github.io/jts/">JTS</a>), and works like
this:</p>
<ul>
<li>if precision is zero (default, unspecified), nothing is
modified</li>
<li>negative values convert to float (4-byte real) precision</li>
<li>positive values convert to
<code>round(x*precision)/precision</code>.</li>
</ul>
<p>For the precision model, see also <a href="https://locationtech.github.io/jts/javadoc/org/locationtech/jts/geom/PrecisionModel.html">here</a>,
where it is written that: “… to specify 3 decimal places of precision,
use a scale factor of 1000. To specify -3 decimal places of precision
(i.e. rounding to the nearest 1000), use a scale factor of 0.001.” Note
that all coordinates, so also <code>Z</code> or <code>M</code> values
(if present) are affected. Choosing values for <code>precision</code>
may require some experimenting.</p>
</div>
</div>
<div id="reading-and-writing" class="section level2">
<h2>Reading and writing</h2>
<p>As we’ve seen above, reading spatial data from an external file can
be done by</p>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>filename <span class="ot">&lt;-</span> <span class="fu">system.file</span>(<span class="st">&quot;shape/nc.shp&quot;</span>, <span class="at">package=</span><span class="st">&quot;sf&quot;</span>)</span>
<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>nc <span class="ot">&lt;-</span> <span class="fu">st_read</span>(filename)</span>
<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a><span class="do">## Reading layer `nc&#39; from data source </span></span>
<span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a><span class="do">##   `/tmp/RtmpoyoHrq/Rinstb81f123adcb20/sf/shape/nc.shp&#39; using driver `ESRI Shapefile&#39;</span></span>
<span id="cb23-5"><a href="#cb23-5" aria-hidden="true" tabindex="-1"></a><span class="do">## Simple feature collection with 100 features and 14 fields</span></span>
<span id="cb23-6"><a href="#cb23-6" aria-hidden="true" tabindex="-1"></a><span class="do">## Geometry type: MULTIPOLYGON</span></span>
<span id="cb23-7"><a href="#cb23-7" aria-hidden="true" tabindex="-1"></a><span class="do">## Dimension:     XY</span></span>
<span id="cb23-8"><a href="#cb23-8" aria-hidden="true" tabindex="-1"></a><span class="do">## Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965</span></span>
<span id="cb23-9"><a href="#cb23-9" aria-hidden="true" tabindex="-1"></a><span class="do">## Geodetic CRS:  NAD27</span></span></code></pre></div>
<p>we can suppress the output by adding argument <code>quiet=TRUE</code>
or by using the otherwise nearly identical but more quiet</p>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>nc <span class="ot">&lt;-</span> <span class="fu">read_sf</span>(filename)</span></code></pre></div>
<p>Writing takes place in the same fashion, using
<code>st_write</code>:</p>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a><span class="fu">st_write</span>(nc, <span class="st">&quot;nc.shp&quot;</span>)</span>
<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a><span class="do">## Writing layer `nc&#39; to data source `nc.shp&#39; using driver `ESRI Shapefile&#39;</span></span>
<span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a><span class="do">## Writing 100 features with 14 fields and geometry type Multi Polygon.</span></span></code></pre></div>
<p>If we repeat this, we get an error message that the file already
exists, and we can overwrite by</p>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="fu">st_write</span>(nc, <span class="st">&quot;nc.shp&quot;</span>, <span class="at">delete_layer =</span> <span class="cn">TRUE</span>)</span>
<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a><span class="do">## Deleting layer `nc&#39; using driver `ESRI Shapefile&#39;</span></span>
<span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a><span class="do">## Writing layer `nc&#39; to data source `nc.shp&#39; using driver `ESRI Shapefile&#39;</span></span>
<span id="cb26-4"><a href="#cb26-4" aria-hidden="true" tabindex="-1"></a><span class="do">## Writing 100 features with 14 fields and geometry type Multi Polygon.</span></span></code></pre></div>
<p>or its quiet alternative that does this by default,</p>
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a><span class="fu">write_sf</span>(nc, <span class="st">&quot;nc.shp&quot;</span>) <span class="co"># silently overwrites</span></span></code></pre></div>
<div id="driver-specific-options" class="section level3">
<h3>Driver-specific options</h3>
<p>The <code>dsn</code> and <code>layer</code> arguments to
<code>st_read</code> and <code>st_write</code> denote a data source name
and optionally a layer name. Their exact interpretation as well as the
options they support vary per driver, the <a href="https://gdal.org/drivers/vector/index.html">GDAL driver
documentation</a> is best consulted for this. For instance, a PostGIS
table in database <code>postgis</code> might be read by</p>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a>meuse <span class="ot">&lt;-</span> <span class="fu">st_read</span>(<span class="st">&quot;PG:dbname=postgis&quot;</span>, <span class="st">&quot;meuse&quot;</span>)</span></code></pre></div>
<p>where the <code>PG:</code> string indicates this concerns the PostGIS
driver, followed by database name, and possibly port and user
credentials. When the <code>layer</code> and <code>driver</code>
arguments are not specified, <code>st_read</code> tries to guess them
from the datasource, or else simply reads the first layer, giving a
warning in case there are more.</p>
<p><code>st_read</code> typically reads the coordinate reference system
as <code>proj4string</code>, but not the EPSG (SRID). GDAL cannot
retrieve SRID (EPSG code) from <code>proj4string</code> strings, and,
when needed, it has to be set by the user. See also the section on <a href="#crs">coordinate reference systems</a>.</p>
<p><code>st_drivers()</code> returns a <code>data.frame</code> listing
available drivers, and their metadata: names, whether a driver can
write, and whether it is a raster and/or vector driver. All drivers can
read. Reading of some common data formats is illustrated below:</p>
<p><code>st_layers(dsn)</code> lists the layers present in data source
<code>dsn</code>, and gives the number of fields, features and geometry
type for each layer:</p>
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="fu">st_layers</span>(<span class="fu">system.file</span>(<span class="st">&quot;osm/overpass.osm&quot;</span>, <span class="at">package=</span><span class="st">&quot;sf&quot;</span>))</span>
<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a><span class="do">## Driver: OSM </span></span>
<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a><span class="do">## Available layers:</span></span>
<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a><span class="do">##         layer_name       geometry_type features fields crs_name</span></span>
<span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a><span class="do">## 1           points               Point       NA     10   WGS 84</span></span>
<span id="cb29-6"><a href="#cb29-6" aria-hidden="true" tabindex="-1"></a><span class="do">## 2            lines         Line String       NA     10   WGS 84</span></span>
<span id="cb29-7"><a href="#cb29-7" aria-hidden="true" tabindex="-1"></a><span class="do">## 3 multilinestrings   Multi Line String       NA      4   WGS 84</span></span>
<span id="cb29-8"><a href="#cb29-8" aria-hidden="true" tabindex="-1"></a><span class="do">## 4    multipolygons       Multi Polygon       NA     25   WGS 84</span></span>
<span id="cb29-9"><a href="#cb29-9" aria-hidden="true" tabindex="-1"></a><span class="do">## 5  other_relations Geometry Collection       NA      4   WGS 84</span></span></code></pre></div>
<p>we see that in this case, the number of features is <code>NA</code>
because for this xml file the whole file needs to be read, which may be
costly for large files. We can force counting by</p>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a><span class="fu">Sys.setenv</span>(<span class="at">OSM_USE_CUSTOM_INDEXING=</span><span class="st">&quot;NO&quot;</span>)</span>
<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a><span class="fu">st_layers</span>(<span class="fu">system.file</span>(<span class="st">&quot;osm/overpass.osm&quot;</span>, <span class="at">package=</span><span class="st">&quot;sf&quot;</span>), <span class="at">do_count =</span> <span class="cn">TRUE</span>)</span>
<span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a><span class="do">## Driver: OSM </span></span>
<span id="cb30-4"><a href="#cb30-4" aria-hidden="true" tabindex="-1"></a><span class="do">## Available layers:</span></span>
<span id="cb30-5"><a href="#cb30-5" aria-hidden="true" tabindex="-1"></a><span class="do">##         layer_name       geometry_type features fields crs_name</span></span>
<span id="cb30-6"><a href="#cb30-6" aria-hidden="true" tabindex="-1"></a><span class="do">## 1           points               Point        1     10   WGS 84</span></span>
<span id="cb30-7"><a href="#cb30-7" aria-hidden="true" tabindex="-1"></a><span class="do">## 2            lines         Line String        0     10   WGS 84</span></span>
<span id="cb30-8"><a href="#cb30-8" aria-hidden="true" tabindex="-1"></a><span class="do">## 3 multilinestrings   Multi Line String        0      4   WGS 84</span></span>
<span id="cb30-9"><a href="#cb30-9" aria-hidden="true" tabindex="-1"></a><span class="do">## 4    multipolygons       Multi Polygon       13     25   WGS 84</span></span>
<span id="cb30-10"><a href="#cb30-10" aria-hidden="true" tabindex="-1"></a><span class="do">## 5  other_relations Geometry Collection        0      4   WGS 84</span></span></code></pre></div>
<p>Another example of reading kml and kmz files is:</p>
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Download .shp data</span></span>
<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a>u_shp <span class="ot">&lt;-</span> <span class="st">&quot;http://coagisweb.cabq.gov/datadownload/biketrails.zip&quot;</span></span>
<span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a><span class="fu">download.file</span>(u_shp, <span class="st">&quot;biketrails.zip&quot;</span>)</span>
<span id="cb31-4"><a href="#cb31-4" aria-hidden="true" tabindex="-1"></a><span class="fu">unzip</span>(<span class="st">&quot;biketrails.zip&quot;</span>)</span>
<span id="cb31-5"><a href="#cb31-5" aria-hidden="true" tabindex="-1"></a>u_kmz <span class="ot">&lt;-</span> <span class="st">&quot;http://coagisweb.cabq.gov/datadownload/BikePaths.kmz&quot;</span></span>
<span id="cb31-6"><a href="#cb31-6" aria-hidden="true" tabindex="-1"></a><span class="fu">download.file</span>(u_kmz, <span class="st">&quot;BikePaths.kmz&quot;</span>)</span>
<span id="cb31-7"><a href="#cb31-7" aria-hidden="true" tabindex="-1"></a><span class="co"># Read file formats</span></span>
<span id="cb31-8"><a href="#cb31-8" aria-hidden="true" tabindex="-1"></a>biketrails_shp <span class="ot">&lt;-</span> <span class="fu">st_read</span>(<span class="st">&quot;biketrails.shp&quot;</span>)</span>
<span id="cb31-9"><a href="#cb31-9" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span>(<span class="fu">Sys.info</span>()[<span class="dv">1</span>] <span class="sc">==</span> <span class="st">&quot;Linux&quot;</span>) <span class="co"># may not work if not Linux</span></span>
<span id="cb31-10"><a href="#cb31-10" aria-hidden="true" tabindex="-1"></a>  biketrails_kmz <span class="ot">&lt;-</span> <span class="fu">st_read</span>(<span class="st">&quot;BikePaths.kmz&quot;</span>)</span>
<span id="cb31-11"><a href="#cb31-11" aria-hidden="true" tabindex="-1"></a>u_kml <span class="ot">=</span> <span class="st">&quot;http://www.northeastraces.com/oxonraces.com/nearme/safe/6.kml&quot;</span></span>
<span id="cb31-12"><a href="#cb31-12" aria-hidden="true" tabindex="-1"></a><span class="fu">download.file</span>(u_kml, <span class="st">&quot;bikeraces.kml&quot;</span>)</span>
<span id="cb31-13"><a href="#cb31-13" aria-hidden="true" tabindex="-1"></a>bikraces <span class="ot">&lt;-</span> <span class="fu">st_read</span>(<span class="st">&quot;bikeraces.kml&quot;</span>)</span></code></pre></div>
</div>
<div id="crud" class="section level3">
<h3>Create, read, update and delete</h3>
<p>GDAL provides the <a href="https://en.wikipedia.org/wiki/Create,_read,_update_and_delete">crud</a>
(create, read, update, delete) functions to persistent storage.
<code>st_read</code> (or <code>read_sf</code>) are used for reading.
<code>st_write</code> (or <code>write_sf</code>) creates, and has the
following arguments to control update and delete:</p>
<ul>
<li><code>update=TRUE</code> causes an existing data source to be
updated, if it exists; this option is by default <code>TRUE</code> for
all database drivers, where the database is updated by adding a
table.</li>
<li><code>delete_layer=TRUE</code> causes <code>st_write</code> try to
open the the data source and delete the layer; no errors are given if
the data source is not present, or the layer does not exist in the data
source.</li>
<li><code>delete_dsn=TRUE</code> causes <code>st_write</code> to delete
the data source when present, before writing the layer in a newly
created data source. No error is given when the data source does not
exist. This option should be handled with care, as it may wipe complete
directories or databases.</li>
</ul>
</div>
<div id="connection-to-spatial-databases" class="section level3">
<h3>Connection to spatial databases</h3>
<p>Read and write functions, <code>st_read()</code> and
<code>st_write()</code>, can handle connections to spatial databases to
read WKB or WKT directly without using GDAL. Although intended to use
the DBI interface, current use and testing of these functions are
limited to PostGIS.</p>
</div>
</div>
<div id="crs" class="section level2">
<h2>Coordinate reference systems and transformations</h2>
<p>Coordinate reference systems (CRS) are like measurement units for
coordinates: they specify which location on Earth a particular
coordinate pair refers to. We saw above that <code>sfc</code> objects
(geometry list-columns) have two attributes to store a CRS:
<code>epsg</code> and <code>proj4string</code>. This implies that all
geometries in a geometry list-column must have the same CRS. Both may be
<code>NA</code>, e.g. in case the CRS is unknown, or when we work with
local coordinate systems (e.g. inside a building, a body, or an abstract
space).</p>
<p><code>proj4string</code> is a generic, string-based description of a
CRS, understood by the <a href="https://proj4.org/">PROJ</a> library. It
defines projection types and (often) defines parameter values for
particular projections, and hence can cover an infinite amount of
different projections. This library (also used by GDAL) provides
functions to convert or transform between different CRS.
<code>epsg</code> is the integer ID for a particular, known CRS that can
be resolved into a <code>proj4string</code>. Some
<code>proj4string</code> values can resolved back into their
corresponding <code>epsg</code> ID, but this does not always work.</p>
<p>The importance of having <code>epsg</code> values stored with data
besides <code>proj4string</code> values is that the <code>epsg</code>
refers to particular, well-known CRS, whose parameters may change
(improve) over time; fixing only the <code>proj4string</code> may remove
the possibility to benefit from such improvements, and limit some of the
provenance of datasets, but may help reproducibility.</p>
<p>Coordinate reference system transformations can be carried out using
<code>st_transform</code>, e.g. converting longitudes/latitudes in NAD27
to web mercator (EPSG:3857) can be done by</p>
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a>nc.web_mercator <span class="ot">&lt;-</span> <span class="fu">st_transform</span>(nc, <span class="dv">3857</span>)</span>
<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a><span class="fu">st_geometry</span>(nc.web_mercator)[[<span class="dv">4</span>]][[<span class="dv">2</span>]][[<span class="dv">1</span>]][<span class="dv">1</span><span class="sc">:</span><span class="dv">3</span>,]</span>
<span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a><span class="do">##          [,1]    [,2]</span></span>
<span id="cb32-4"><a href="#cb32-4" aria-hidden="true" tabindex="-1"></a><span class="do">## [1,] -8463267 4377507</span></span>
<span id="cb32-5"><a href="#cb32-5" aria-hidden="true" tabindex="-1"></a><span class="do">## [2,] -8460094 4377498</span></span>
<span id="cb32-6"><a href="#cb32-6" aria-hidden="true" tabindex="-1"></a><span class="do">## [3,] -8450437 4375541</span></span></code></pre></div>
</div>
<div id="conversion-including-to-and-from-sp" class="section level2">
<h2>Conversion, including to and from sp</h2>
<p><code>sf</code> objects and objects deriving from
<code>Spatial</code> (package <code>sp</code>) can be coerced both
ways:</p>
<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a><span class="fu">showMethods</span>(<span class="st">&quot;coerce&quot;</span>, <span class="at">classes =</span> <span class="st">&quot;sf&quot;</span>)</span>
<span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a><span class="do">## Function: coerce (package methods)</span></span>
<span id="cb33-3"><a href="#cb33-3" aria-hidden="true" tabindex="-1"></a><span class="do">## from=&quot;Spatial&quot;, to=&quot;sf&quot;</span></span>
<span id="cb33-4"><a href="#cb33-4" aria-hidden="true" tabindex="-1"></a><span class="do">## from=&quot;sf&quot;, to=&quot;Spatial&quot;</span></span>
<span id="cb33-5"><a href="#cb33-5" aria-hidden="true" tabindex="-1"></a><span class="fu">methods</span>(st_as_sf)</span>
<span id="cb33-6"><a href="#cb33-6" aria-hidden="true" tabindex="-1"></a><span class="do">##  [1] st_as_sf.SpatVector*   st_as_sf.Spatial*      st_as_sf.data.frame*  </span></span>
<span id="cb33-7"><a href="#cb33-7" aria-hidden="true" tabindex="-1"></a><span class="do">##  [4] st_as_sf.lpp*          st_as_sf.map*          st_as_sf.owin*        </span></span>
<span id="cb33-8"><a href="#cb33-8" aria-hidden="true" tabindex="-1"></a><span class="do">##  [7] st_as_sf.ppp*          st_as_sf.ppplist*      st_as_sf.psp*         </span></span>
<span id="cb33-9"><a href="#cb33-9" aria-hidden="true" tabindex="-1"></a><span class="do">## [10] st_as_sf.s2_geography* st_as_sf.sf*           st_as_sf.sfc*         </span></span>
<span id="cb33-10"><a href="#cb33-10" aria-hidden="true" tabindex="-1"></a><span class="do">## see &#39;?methods&#39; for accessing help and source code</span></span>
<span id="cb33-11"><a href="#cb33-11" aria-hidden="true" tabindex="-1"></a><span class="fu">methods</span>(st_as_sfc)</span>
<span id="cb33-12"><a href="#cb33-12" aria-hidden="true" tabindex="-1"></a><span class="do">##  [1] st_as_sfc.SpatialLines*       st_as_sfc.SpatialMultiPoints*</span></span>
<span id="cb33-13"><a href="#cb33-13" aria-hidden="true" tabindex="-1"></a><span class="do">##  [3] st_as_sfc.SpatialPixels*      st_as_sfc.SpatialPoints*     </span></span>
<span id="cb33-14"><a href="#cb33-14" aria-hidden="true" tabindex="-1"></a><span class="do">##  [5] st_as_sfc.SpatialPolygons*    st_as_sfc.WKB*               </span></span>
<span id="cb33-15"><a href="#cb33-15" aria-hidden="true" tabindex="-1"></a><span class="do">##  [7] st_as_sfc.bbox*               st_as_sfc.blob*              </span></span>
<span id="cb33-16"><a href="#cb33-16" aria-hidden="true" tabindex="-1"></a><span class="do">##  [9] st_as_sfc.character*          st_as_sfc.dimensions*        </span></span>
<span id="cb33-17"><a href="#cb33-17" aria-hidden="true" tabindex="-1"></a><span class="do">## [11] st_as_sfc.factor*             st_as_sfc.list*              </span></span>
<span id="cb33-18"><a href="#cb33-18" aria-hidden="true" tabindex="-1"></a><span class="do">## [13] st_as_sfc.map*                st_as_sfc.owin*              </span></span>
<span id="cb33-19"><a href="#cb33-19" aria-hidden="true" tabindex="-1"></a><span class="do">## [15] st_as_sfc.pq_geometry*        st_as_sfc.psp*               </span></span>
<span id="cb33-20"><a href="#cb33-20" aria-hidden="true" tabindex="-1"></a><span class="do">## [17] st_as_sfc.raw*                st_as_sfc.s2_geography*      </span></span>
<span id="cb33-21"><a href="#cb33-21" aria-hidden="true" tabindex="-1"></a><span class="do">## [19] st_as_sfc.sf*                 st_as_sfc.tess*              </span></span>
<span id="cb33-22"><a href="#cb33-22" aria-hidden="true" tabindex="-1"></a><span class="do">## see &#39;?methods&#39; for accessing help and source code</span></span>
<span id="cb33-23"><a href="#cb33-23" aria-hidden="true" tabindex="-1"></a><span class="co"># anticipate that sp::CRS will expand proj4strings:</span></span>
<span id="cb33-24"><a href="#cb33-24" aria-hidden="true" tabindex="-1"></a>p4s <span class="ot">&lt;-</span> <span class="st">&quot;+proj=longlat +datum=NAD27 +no_defs +ellps=clrk66 +nadgrids=@conus,@alaska,@ntv2_0.gsb,@ntv1_can.dat&quot;</span></span>
<span id="cb33-25"><a href="#cb33-25" aria-hidden="true" tabindex="-1"></a><span class="fu">st_crs</span>(nc) <span class="ot">&lt;-</span> p4s</span>
<span id="cb33-26"><a href="#cb33-26" aria-hidden="true" tabindex="-1"></a><span class="co"># anticipate geometry column name changes:</span></span>
<span id="cb33-27"><a href="#cb33-27" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(nc)[<span class="dv">15</span>] <span class="ot">=</span> <span class="st">&quot;geometry&quot;</span></span>
<span id="cb33-28"><a href="#cb33-28" aria-hidden="true" tabindex="-1"></a><span class="fu">attr</span>(nc, <span class="st">&quot;sf_column&quot;</span>) <span class="ot">=</span> <span class="st">&quot;geometry&quot;</span></span>
<span id="cb33-29"><a href="#cb33-29" aria-hidden="true" tabindex="-1"></a>nc.sp <span class="ot">&lt;-</span> <span class="fu">as</span>(nc, <span class="st">&quot;Spatial&quot;</span>)</span>
<span id="cb33-30"><a href="#cb33-30" aria-hidden="true" tabindex="-1"></a><span class="fu">class</span>(nc.sp)</span>
<span id="cb33-31"><a href="#cb33-31" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] &quot;SpatialPolygonsDataFrame&quot;</span></span>
<span id="cb33-32"><a href="#cb33-32" aria-hidden="true" tabindex="-1"></a><span class="do">## attr(,&quot;package&quot;)</span></span>
<span id="cb33-33"><a href="#cb33-33" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] &quot;sp&quot;</span></span>
<span id="cb33-34"><a href="#cb33-34" aria-hidden="true" tabindex="-1"></a>nc2 <span class="ot">&lt;-</span> <span class="fu">st_as_sf</span>(nc.sp)</span>
<span id="cb33-35"><a href="#cb33-35" aria-hidden="true" tabindex="-1"></a><span class="fu">all.equal</span>(nc, nc2)</span>
<span id="cb33-36"><a href="#cb33-36" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] &quot;Attributes: &lt; Component \&quot;class\&quot;: Lengths (4, 2) differ (string compare on first 2) &gt;&quot;           </span></span>
<span id="cb33-37"><a href="#cb33-37" aria-hidden="true" tabindex="-1"></a><span class="do">## [2] &quot;Attributes: &lt; Component \&quot;class\&quot;: 1 string mismatch &gt;&quot;                                           </span></span>
<span id="cb33-38"><a href="#cb33-38" aria-hidden="true" tabindex="-1"></a><span class="do">## [3] &quot;Component \&quot;geometry\&quot;: Attributes: &lt; Component \&quot;crs\&quot;: Component \&quot;input\&quot;: 1 string mismatch &gt;&quot;</span></span>
<span id="cb33-39"><a href="#cb33-39" aria-hidden="true" tabindex="-1"></a><span class="do">## [4] &quot;Component \&quot;geometry\&quot;: Attributes: &lt; Component \&quot;crs\&quot;: Component \&quot;wkt\&quot;: 1 string mismatch &gt;&quot;</span></span></code></pre></div>
<p>As the <code>Spatial*</code> objects only support
<code>MULTILINESTRING</code> and <code>MULTIPOLYGON</code>,
<code>LINESTRING</code> and <code>POLYGON</code> geometries are
automatically coerced into their <code>MULTI</code> form. When
converting <code>Spatial*</code> into <code>sf</code>, if all geometries
consist of a single <code>POLYGON</code> (possibly with holes), a
<code>POLYGON</code> and otherwise all geometries are returned as
<code>MULTIPOLYGON</code>: a mix of <code>POLYGON</code> and
<code>MULTIPOLYGON</code> (such as common in shapefiles) is not created.
Argument <code>forceMulti=TRUE</code> will override this, and create
<code>MULTIPOLYGON</code>s in all cases. For <code>LINES</code> the
situation is identical.</p>
</div>
<div id="geometrycollection" class="section level2">
<h2>Geometrical operations</h2>
<p>The standard for simple feature access defines a number of
geometrical operations.</p>
<p><code>st_is_valid</code> and <code>st_is_simple</code> return a
boolean indicating whether a geometry is valid or simple.</p>
<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a><span class="fu">st_is_valid</span>(nc[<span class="dv">1</span><span class="sc">:</span><span class="dv">2</span>,])</span>
<span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] TRUE TRUE</span></span></code></pre></div>
<p><code>st_distance</code> returns a dense numeric matrix with
distances between geometries. <code>st_relate</code> returns a character
matrix with the <a href="https://en.wikipedia.org/wiki/DE-9IM#Illustration">DE9-IM</a>
values for each pair of geometries:</p>
<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a>x <span class="ot">=</span> <span class="fu">st_transform</span>(nc, <span class="dv">32119</span>)</span>
<span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a><span class="fu">st_distance</span>(x[<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">4</span>,<span class="dv">22</span>),], x[<span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">33</span>,<span class="dv">55</span>,<span class="dv">56</span>),])</span>
<span id="cb35-3"><a href="#cb35-3" aria-hidden="true" tabindex="-1"></a><span class="do">## Units: [m]</span></span>
<span id="cb35-4"><a href="#cb35-4" aria-hidden="true" tabindex="-1"></a><span class="do">##           [,1]     [,2]      [,3]     [,4]</span></span>
<span id="cb35-5"><a href="#cb35-5" aria-hidden="true" tabindex="-1"></a><span class="do">## [1,]      0.00 312176.2 128338.51 475608.8</span></span>
<span id="cb35-6"><a href="#cb35-6" aria-hidden="true" tabindex="-1"></a><span class="do">## [2,] 440548.35 114938.1 590417.79      0.0</span></span>
<span id="cb35-7"><a href="#cb35-7" aria-hidden="true" tabindex="-1"></a><span class="do">## [3,]  18943.74 352708.6  78754.75 517511.6</span></span>
<span id="cb35-8"><a href="#cb35-8" aria-hidden="true" tabindex="-1"></a><span class="fu">st_relate</span>(nc[<span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>,], nc[<span class="dv">1</span><span class="sc">:</span><span class="dv">4</span>,])</span>
<span id="cb35-9"><a href="#cb35-9" aria-hidden="true" tabindex="-1"></a><span class="do">## although coordinates are longitude/latitude, st_relate assumes that they are</span></span>
<span id="cb35-10"><a href="#cb35-10" aria-hidden="true" tabindex="-1"></a><span class="do">## planar</span></span>
<span id="cb35-11"><a href="#cb35-11" aria-hidden="true" tabindex="-1"></a><span class="do">##      [,1]        [,2]        [,3]        [,4]       </span></span>
<span id="cb35-12"><a href="#cb35-12" aria-hidden="true" tabindex="-1"></a><span class="do">## [1,] &quot;2FFF1FFF2&quot; &quot;FF2F11212&quot; &quot;FF2FF1212&quot; &quot;FF2FF1212&quot;</span></span>
<span id="cb35-13"><a href="#cb35-13" aria-hidden="true" tabindex="-1"></a><span class="do">## [2,] &quot;FF2F11212&quot; &quot;2FFF1FFF2&quot; &quot;FF2F11212&quot; &quot;FF2FF1212&quot;</span></span>
<span id="cb35-14"><a href="#cb35-14" aria-hidden="true" tabindex="-1"></a><span class="do">## [3,] &quot;FF2FF1212&quot; &quot;FF2F11212&quot; &quot;2FFF1FFF2&quot; &quot;FF2FF1212&quot;</span></span>
<span id="cb35-15"><a href="#cb35-15" aria-hidden="true" tabindex="-1"></a><span class="do">## [4,] &quot;FF2FF1212&quot; &quot;FF2FF1212&quot; &quot;FF2FF1212&quot; &quot;2FFF1FFF2&quot;</span></span>
<span id="cb35-16"><a href="#cb35-16" aria-hidden="true" tabindex="-1"></a><span class="do">## [5,] &quot;FF2FF1212&quot; &quot;FF2FF1212&quot; &quot;FF2FF1212&quot; &quot;FF2FF1212&quot;</span></span></code></pre></div>
<p>The commands <code>st_intersects</code>, <code>st_disjoint</code>,
<code>st_touches</code>, <code>st_crosses</code>,
<code>st_within</code>, <code>st_contains</code>,
<code>st_overlaps</code>, <code>st_equals</code>,
<code>st_covers</code>, <code>st_covered_by</code>,
<code>st_equals_exact</code> and <code>st_is_within_distance</code>
return a sparse matrix with matching (TRUE) indexes, or a full logical
matrix:</p>
<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a><span class="fu">st_intersects</span>(nc[<span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>,], nc[<span class="dv">1</span><span class="sc">:</span><span class="dv">4</span>,])</span>
<span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a><span class="do">## Sparse geometry binary predicate list of length 5, where the predicate</span></span>
<span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a><span class="do">## was `intersects&#39;</span></span>
<span id="cb36-4"><a href="#cb36-4" aria-hidden="true" tabindex="-1"></a><span class="do">##  1: 1, 2</span></span>
<span id="cb36-5"><a href="#cb36-5" aria-hidden="true" tabindex="-1"></a><span class="do">##  2: 1, 2, 3</span></span>
<span id="cb36-6"><a href="#cb36-6" aria-hidden="true" tabindex="-1"></a><span class="do">##  3: 2, 3</span></span>
<span id="cb36-7"><a href="#cb36-7" aria-hidden="true" tabindex="-1"></a><span class="do">##  4: 4</span></span>
<span id="cb36-8"><a href="#cb36-8" aria-hidden="true" tabindex="-1"></a><span class="do">##  5: (empty)</span></span>
<span id="cb36-9"><a href="#cb36-9" aria-hidden="true" tabindex="-1"></a><span class="fu">st_intersects</span>(nc[<span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>,], nc[<span class="dv">1</span><span class="sc">:</span><span class="dv">4</span>,], <span class="at">sparse =</span> <span class="cn">FALSE</span>)</span>
<span id="cb36-10"><a href="#cb36-10" aria-hidden="true" tabindex="-1"></a><span class="do">##       [,1]  [,2]  [,3]  [,4]</span></span>
<span id="cb36-11"><a href="#cb36-11" aria-hidden="true" tabindex="-1"></a><span class="do">## [1,]  TRUE  TRUE FALSE FALSE</span></span>
<span id="cb36-12"><a href="#cb36-12" aria-hidden="true" tabindex="-1"></a><span class="do">## [2,]  TRUE  TRUE  TRUE FALSE</span></span>
<span id="cb36-13"><a href="#cb36-13" aria-hidden="true" tabindex="-1"></a><span class="do">## [3,] FALSE  TRUE  TRUE FALSE</span></span>
<span id="cb36-14"><a href="#cb36-14" aria-hidden="true" tabindex="-1"></a><span class="do">## [4,] FALSE FALSE FALSE  TRUE</span></span>
<span id="cb36-15"><a href="#cb36-15" aria-hidden="true" tabindex="-1"></a><span class="do">## [5,] FALSE FALSE FALSE FALSE</span></span></code></pre></div>
<p>The commands <code>st_buffer</code>, <code>st_boundary</code>,
<code>st_convexhull</code>, <code>st_union_cascaded</code>,
<code>st_simplify</code>, <code>st_triangulate</code>,
<code>st_polygonize</code>, <code>st_centroid</code>,
<code>st_segmentize</code>, and <code>st_union</code> return new
geometries, e.g.:</p>
<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a>sel <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">5</span>,<span class="dv">14</span>)</span>
<span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a>geom <span class="ot">=</span> <span class="fu">st_geometry</span>(nc.web_mercator[sel,])</span>
<span id="cb37-3"><a href="#cb37-3" aria-hidden="true" tabindex="-1"></a>buf <span class="ot">&lt;-</span> <span class="fu">st_buffer</span>(geom, <span class="at">dist =</span> <span class="dv">30000</span>)</span>
<span id="cb37-4"><a href="#cb37-4" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(buf, <span class="at">border =</span> <span class="st">&#39;red&#39;</span>)</span>
<span id="cb37-5"><a href="#cb37-5" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(geom, <span class="at">add =</span> <span class="cn">TRUE</span>)</span>
<span id="cb37-6"><a href="#cb37-6" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(<span class="fu">st_buffer</span>(geom, <span class="sc">-</span><span class="dv">5000</span>), <span class="at">add =</span> <span class="cn">TRUE</span>, <span class="at">border =</span> <span class="st">&#39;blue&#39;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<p>Commands <code>st_intersection</code>, <code>st_union</code>,
<code>st_difference</code>, <code>st_sym_difference</code> return new
geometries that are a function of pairs of geometries:</p>
<div class="sourceCode" id="cb38"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a><span class="fu">par</span>(<span class="at">mar =</span> <span class="fu">rep</span>(<span class="dv">0</span>,<span class="dv">4</span>))</span>
<span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a>u <span class="ot">&lt;-</span> <span class="fu">st_union</span>(nc)</span>
<span id="cb38-3"><a href="#cb38-3" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(u)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>The following code shows how computing an intersection between two
polygons may yield a <code>GEOMETRYCOLLECTION</code> with a point, line
and polygon:</p>
<div class="sourceCode" id="cb39"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb39-1"><a href="#cb39-1" aria-hidden="true" tabindex="-1"></a>opar <span class="ot">&lt;-</span> <span class="fu">par</span>(<span class="at">mfrow =</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">2</span>))</span>
<span id="cb39-2"><a href="#cb39-2" aria-hidden="true" tabindex="-1"></a>a <span class="ot">&lt;-</span> <span class="fu">st_polygon</span>(<span class="fu">list</span>(<span class="fu">cbind</span>(<span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="fl">7.5</span>,<span class="fl">7.5</span>,<span class="dv">0</span>),<span class="fu">c</span>(<span class="dv">0</span>,<span class="sc">-</span><span class="dv">1</span>,<span class="sc">-</span><span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>))))</span>
<span id="cb39-3"><a href="#cb39-3" aria-hidden="true" tabindex="-1"></a>b <span class="ot">&lt;-</span> <span class="fu">st_polygon</span>(<span class="fu">list</span>(<span class="fu">cbind</span>(<span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">2</span>,<span class="dv">3</span>,<span class="dv">4</span>,<span class="dv">5</span>,<span class="dv">6</span>,<span class="dv">7</span>,<span class="dv">7</span>,<span class="dv">0</span>),<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">0</span>,.<span class="dv">5</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="fl">0.5</span>,<span class="sc">-</span><span class="fl">0.5</span>,<span class="sc">-</span><span class="fl">0.5</span>,<span class="dv">1</span>,<span class="dv">1</span>))))</span>
<span id="cb39-4"><a href="#cb39-4" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(a, <span class="at">ylim =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">1</span>,<span class="dv">1</span>))</span>
<span id="cb39-5"><a href="#cb39-5" aria-hidden="true" tabindex="-1"></a><span class="fu">title</span>(<span class="st">&quot;intersecting two polygons:&quot;</span>)</span>
<span id="cb39-6"><a href="#cb39-6" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(b, <span class="at">add =</span> <span class="cn">TRUE</span>, <span class="at">border =</span> <span class="st">&#39;red&#39;</span>)</span>
<span id="cb39-7"><a href="#cb39-7" aria-hidden="true" tabindex="-1"></a>(i <span class="ot">&lt;-</span> <span class="fu">st_intersection</span>(a,b))</span>
<span id="cb39-8"><a href="#cb39-8" aria-hidden="true" tabindex="-1"></a><span class="do">## GEOMETRYCOLLECTION (POLYGON ((7 0, 7 -0.5, 6 -0.5, 5.5 0, 7 0)), LINESTRING (4 0, 3 0), POINT (1 0))</span></span>
<span id="cb39-9"><a href="#cb39-9" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(a, <span class="at">ylim =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">1</span>,<span class="dv">1</span>))</span>
<span id="cb39-10"><a href="#cb39-10" aria-hidden="true" tabindex="-1"></a><span class="fu">title</span>(<span class="st">&quot;GEOMETRYCOLLECTION&quot;</span>)</span>
<span id="cb39-11"><a href="#cb39-11" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(b, <span class="at">add =</span> <span class="cn">TRUE</span>, <span class="at">border =</span> <span class="st">&#39;red&#39;</span>)</span>
<span id="cb39-12"><a href="#cb39-12" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(i, <span class="at">add =</span> <span class="cn">TRUE</span>, <span class="at">col =</span> <span class="st">&#39;green&#39;</span>, <span class="at">lwd =</span> <span class="dv">2</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb40"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a><span class="fu">par</span>(opar)</span></code></pre></div>
</div>
<div id="non-simple-and-non-valid-geometries" class="section level2">
<h2>Non-simple and non-valid geometries</h2>
<p>Non-simple geometries are for instance self-intersecting lines
(left); non-valid geometries are for instance polygons with slivers
(middle) or self-intersections (right).</p>
<div class="sourceCode" id="cb41"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(sf)</span>
<span id="cb41-2"><a href="#cb41-2" aria-hidden="true" tabindex="-1"></a>x1 <span class="ot">&lt;-</span> <span class="fu">st_linestring</span>(<span class="fu">cbind</span>(<span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">1</span>),<span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">0</span>)))</span>
<span id="cb41-3"><a href="#cb41-3" aria-hidden="true" tabindex="-1"></a>x2 <span class="ot">&lt;-</span> <span class="fu">st_polygon</span>(<span class="fu">list</span>(<span class="fu">cbind</span>(<span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>),<span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.6</span>,<span class="dv">1</span>,<span class="dv">0</span>))))</span>
<span id="cb41-4"><a href="#cb41-4" aria-hidden="true" tabindex="-1"></a>x3 <span class="ot">&lt;-</span> <span class="fu">st_polygon</span>(<span class="fu">list</span>(<span class="fu">cbind</span>(<span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>),<span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>))))</span>
<span id="cb41-5"><a href="#cb41-5" aria-hidden="true" tabindex="-1"></a><span class="fu">st_is_simple</span>(<span class="fu">st_sfc</span>(x1))</span>
<span id="cb41-6"><a href="#cb41-6" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] FALSE</span></span>
<span id="cb41-7"><a href="#cb41-7" aria-hidden="true" tabindex="-1"></a><span class="fu">st_is_valid</span>(<span class="fu">st_sfc</span>(x2,x3))</span>
<span id="cb41-8"><a href="#cb41-8" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] FALSE FALSE</span></span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
</div>
<div id="units" class="section level1">
<h1>Units</h1>
<p>Where possible geometric operations such as
<code>st_distance()</code>, <code>st_length()</code> and
<code>st_area()</code> report results with a units attribute appropriate
for the CRS:</p>
<div class="sourceCode" id="cb42"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a>a <span class="ot">&lt;-</span> <span class="fu">st_area</span>(nc[<span class="dv">1</span>,])</span>
<span id="cb42-2"><a href="#cb42-2" aria-hidden="true" tabindex="-1"></a><span class="fu">attributes</span>(a)</span>
<span id="cb42-3"><a href="#cb42-3" aria-hidden="true" tabindex="-1"></a><span class="do">## $units</span></span>
<span id="cb42-4"><a href="#cb42-4" aria-hidden="true" tabindex="-1"></a><span class="do">## $numerator</span></span>
<span id="cb42-5"><a href="#cb42-5" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] &quot;m&quot; &quot;m&quot;</span></span>
<span id="cb42-6"><a href="#cb42-6" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span>
<span id="cb42-7"><a href="#cb42-7" aria-hidden="true" tabindex="-1"></a><span class="do">## $denominator</span></span>
<span id="cb42-8"><a href="#cb42-8" aria-hidden="true" tabindex="-1"></a><span class="do">## character(0)</span></span>
<span id="cb42-9"><a href="#cb42-9" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span>
<span id="cb42-10"><a href="#cb42-10" aria-hidden="true" tabindex="-1"></a><span class="do">## attr(,&quot;class&quot;)</span></span>
<span id="cb42-11"><a href="#cb42-11" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] &quot;symbolic_units&quot;</span></span>
<span id="cb42-12"><a href="#cb42-12" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span>
<span id="cb42-13"><a href="#cb42-13" aria-hidden="true" tabindex="-1"></a><span class="do">## $class</span></span>
<span id="cb42-14"><a href="#cb42-14" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] &quot;units&quot;</span></span></code></pre></div>
<p>The <strong>units</strong> package can be used to convert between
units:</p>
<div class="sourceCode" id="cb43"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb43-1"><a href="#cb43-1" aria-hidden="true" tabindex="-1"></a>units<span class="sc">::</span><span class="fu">set_units</span>(a, km<span class="sc">^</span><span class="dv">2</span>) <span class="co"># result in square kilometers</span></span>
<span id="cb43-2"><a href="#cb43-2" aria-hidden="true" tabindex="-1"></a><span class="do">## 1137.108 [km^2]</span></span>
<span id="cb43-3"><a href="#cb43-3" aria-hidden="true" tabindex="-1"></a>units<span class="sc">::</span><span class="fu">set_units</span>(a, ha) <span class="co"># result in hectares</span></span>
<span id="cb43-4"><a href="#cb43-4" aria-hidden="true" tabindex="-1"></a><span class="do">## 113710.8 [ha]</span></span></code></pre></div>
<p>The result can be stripped of their attributes if needs be:</p>
<div class="sourceCode" id="cb44"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb44-1"><a href="#cb44-1" aria-hidden="true" tabindex="-1"></a><span class="fu">as.numeric</span>(a)</span>
<span id="cb44-2"><a href="#cb44-2" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] 1137107793</span></span></code></pre></div>
</div>
<div id="how-attributes-relate-to-geometries" class="section level1">
<h1>How attributes relate to geometries</h1>
<p>(This will eventually be the topic of a new vignette; now here to
explain the last attribute of <code>sf</code> objects)</p>
<p>The standard documents about simple features are very detailed about
the geometric aspects of features, but say nearly nothing about
attributes, except that their values should be understood in another
reference system (their units of measurement, e.g. as implemented in the
package <a href="https://CRAN.R-project.org/package=units"><strong>units</strong></a>).
But there is more to it. For variables like air temperature,
interpolation usually makes sense, for others like human body
temperature it doesn’t. The difference is that air temperature is a
field, which continues between sensors, where body temperature is an
object property that doesn’t extend beyond the body – in spatial
statistics bodies would be called a point pattern, their temperature the
point marks. For geometries that have a non-zero size (positive length
or area), attribute values may refer to the every sub-geometry (every
point), or may summarize the geometry. For example, a state’s population
density summarizes the whole state, and is not a meaningful estimate of
population density for a give point inside the state without the context
of the state. On the other hand, land use or geological maps give
polygons with constant land use or geology, every point inside the
polygon is of that class. Some properties are spatially <a href="https://en.wikipedia.org/wiki/Intensive_and_extensive_properties">extensive</a>,
meaning that attributes would summed up when two geometries are merged:
population is an example. Other properties are spatially intensive, and
should be averaged, with population density the example.</p>
<p>Simple feature objects of class <code>sf</code> have an <em>agr</em>
attribute that points to the <em>attribute-geometry-relationship</em>,
how attributes relate to their geometry. It can be defined at creation
time:</p>
<div class="sourceCode" id="cb45"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb45-1"><a href="#cb45-1" aria-hidden="true" tabindex="-1"></a>nc <span class="ot">&lt;-</span> <span class="fu">st_read</span>(<span class="fu">system.file</span>(<span class="st">&quot;shape/nc.shp&quot;</span>, <span class="at">package=</span><span class="st">&quot;sf&quot;</span>),</span>
<span id="cb45-2"><a href="#cb45-2" aria-hidden="true" tabindex="-1"></a>    <span class="at">agr =</span> <span class="fu">c</span>(<span class="at">AREA =</span> <span class="st">&quot;aggregate&quot;</span>, <span class="at">PERIMETER =</span> <span class="st">&quot;aggregate&quot;</span>, <span class="at">CNTY_ =</span> <span class="st">&quot;identity&quot;</span>,</span>
<span id="cb45-3"><a href="#cb45-3" aria-hidden="true" tabindex="-1"></a>        <span class="at">CNTY_ID =</span> <span class="st">&quot;identity&quot;</span>, <span class="at">NAME =</span> <span class="st">&quot;identity&quot;</span>, <span class="at">FIPS =</span> <span class="st">&quot;identity&quot;</span>, <span class="at">FIPSNO =</span> <span class="st">&quot;identity&quot;</span>,</span>
<span id="cb45-4"><a href="#cb45-4" aria-hidden="true" tabindex="-1"></a>        <span class="at">CRESS_ID =</span> <span class="st">&quot;identity&quot;</span>, <span class="at">BIR74 =</span> <span class="st">&quot;aggregate&quot;</span>, <span class="at">SID74 =</span> <span class="st">&quot;aggregate&quot;</span>, <span class="at">NWBIR74 =</span> <span class="st">&quot;aggregate&quot;</span>,</span>
<span id="cb45-5"><a href="#cb45-5" aria-hidden="true" tabindex="-1"></a>        <span class="at">BIR79 =</span> <span class="st">&quot;aggregate&quot;</span>, <span class="at">SID79 =</span> <span class="st">&quot;aggregate&quot;</span>, <span class="at">NWBIR79 =</span> <span class="st">&quot;aggregate&quot;</span>))</span>
<span id="cb45-6"><a href="#cb45-6" aria-hidden="true" tabindex="-1"></a><span class="do">## Reading layer `nc&#39; from data source </span></span>
<span id="cb45-7"><a href="#cb45-7" aria-hidden="true" tabindex="-1"></a><span class="do">##   `/tmp/RtmpoyoHrq/Rinstb81f123adcb20/sf/shape/nc.shp&#39; using driver `ESRI Shapefile&#39;</span></span>
<span id="cb45-8"><a href="#cb45-8" aria-hidden="true" tabindex="-1"></a><span class="do">## Simple feature collection with 100 features and 14 fields</span></span>
<span id="cb45-9"><a href="#cb45-9" aria-hidden="true" tabindex="-1"></a><span class="do">## Attribute-geometry relationship: 0 constant, 8 aggregate, 6 identity</span></span>
<span id="cb45-10"><a href="#cb45-10" aria-hidden="true" tabindex="-1"></a><span class="do">## Geometry type: MULTIPOLYGON</span></span>
<span id="cb45-11"><a href="#cb45-11" aria-hidden="true" tabindex="-1"></a><span class="do">## Dimension:     XY</span></span>
<span id="cb45-12"><a href="#cb45-12" aria-hidden="true" tabindex="-1"></a><span class="do">## Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965</span></span>
<span id="cb45-13"><a href="#cb45-13" aria-hidden="true" tabindex="-1"></a><span class="do">## Geodetic CRS:  NAD27</span></span>
<span id="cb45-14"><a href="#cb45-14" aria-hidden="true" tabindex="-1"></a><span class="fu">st_agr</span>(nc)</span>
<span id="cb45-15"><a href="#cb45-15" aria-hidden="true" tabindex="-1"></a><span class="do">##      AREA PERIMETER     CNTY_   CNTY_ID      NAME      FIPS    FIPSNO  CRESS_ID </span></span>
<span id="cb45-16"><a href="#cb45-16" aria-hidden="true" tabindex="-1"></a><span class="do">## aggregate aggregate  identity  identity  identity  identity  identity  identity </span></span>
<span id="cb45-17"><a href="#cb45-17" aria-hidden="true" tabindex="-1"></a><span class="do">##     BIR74     SID74   NWBIR74     BIR79     SID79   NWBIR79 </span></span>
<span id="cb45-18"><a href="#cb45-18" aria-hidden="true" tabindex="-1"></a><span class="do">## aggregate aggregate aggregate aggregate aggregate aggregate </span></span>
<span id="cb45-19"><a href="#cb45-19" aria-hidden="true" tabindex="-1"></a><span class="do">## Levels: constant aggregate identity</span></span>
<span id="cb45-20"><a href="#cb45-20" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(meuse, <span class="at">package =</span> <span class="st">&quot;sp&quot;</span>)</span>
<span id="cb45-21"><a href="#cb45-21" aria-hidden="true" tabindex="-1"></a>meuse_sf <span class="ot">&lt;-</span> <span class="fu">st_as_sf</span>(meuse, <span class="at">coords =</span> <span class="fu">c</span>(<span class="st">&quot;x&quot;</span>, <span class="st">&quot;y&quot;</span>), <span class="at">crs =</span> <span class="dv">28992</span>, <span class="at">agr =</span> <span class="st">&quot;constant&quot;</span>)</span>
<span id="cb45-22"><a href="#cb45-22" aria-hidden="true" tabindex="-1"></a><span class="fu">st_agr</span>(meuse_sf)</span>
<span id="cb45-23"><a href="#cb45-23" aria-hidden="true" tabindex="-1"></a><span class="do">##  cadmium   copper     lead     zinc     elev     dist       om    ffreq </span></span>
<span id="cb45-24"><a href="#cb45-24" aria-hidden="true" tabindex="-1"></a><span class="do">## constant constant constant constant constant constant constant constant </span></span>
<span id="cb45-25"><a href="#cb45-25" aria-hidden="true" tabindex="-1"></a><span class="do">##     soil     lime  landuse   dist.m </span></span>
<span id="cb45-26"><a href="#cb45-26" aria-hidden="true" tabindex="-1"></a><span class="do">## constant constant constant constant </span></span>
<span id="cb45-27"><a href="#cb45-27" aria-hidden="true" tabindex="-1"></a><span class="do">## Levels: constant aggregate identity</span></span></code></pre></div>
<p>When not specified, this field is filled with <code>NA</code> values,
but if non-<code>NA</code>, it has one of three possibilities</p>
<table>
<colgroup>
<col width="17%" />
<col width="82%" />
</colgroup>
<thead>
<tr class="header">
<th>value</th>
<th>meaning</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>constant</td>
<td>a variable that has a constant value at every location over a
spatial extent; examples: soil type, climate zone, land use</td>
</tr>
<tr class="even">
<td>aggregate</td>
<td>values are summary values (aggregates) over the geometry,
e.g. population density, dominant land use</td>
</tr>
<tr class="odd">
<td>identity</td>
<td>values identify the geometry: they refer to (the whole of) this and
only this geometry</td>
</tr>
</tbody>
</table>
<p>With this information (still to be done) we can for instance</p>
<ul>
<li>either return missing values or generate warnings when a
<em>aggregate</em> value at a point location inside a polygon is
retrieved, or</li>
<li>list the implicit assumptions made when retrieving attribute values
at points inside a polygon when <code>relation_to_geometry</code> is
missing.</li>
<li>decide what to do with attributes when a geometry is split: do
nothing in case the attribute is constant, give an error or warning in
case it is an aggregate, change the <code>relation_to_geometry</code> to
<em>constant</em> in case it was <em>identity</em>.</li>
</ul>
<p>Further reading:</p>
<ol style="list-style-type: decimal">
<li>S. Scheider, B. Gräler, E. Pebesma, C. Stasch, 2016. Modelling
spatio-temporal information generation. Int J of Geographic Information
Science, 30 (10), 1980-2008. (<a href="https://www.tandfonline.com/doi/full/10.1080/13658816.2016.1151520">open
access</a>)</li>
<li>Stasch, C., S. Scheider, E. Pebesma, W. Kuhn, 2014. Meaningful
Spatial Prediction and Aggregation. Environmental Modelling &amp;
Software, 51, (149–165, <a href="http://dx.doi.org/10.1016/j.envsoft.2013.09.006">open
access</a>).</li>
</ol>
</div>



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